February 5, 2025 • 42 mins
134th Episode - How to Get an Analytics Job Podcast | Featuring Cristina Samano-Romo, Senior Data Analyst at Amisive
In this episode of the How to Get an Analytics Job Podcast (HTGAJ), we sit down with Cristina Samano-Romo, a Senior Data Analyst at Amisive. Cristina shares her journey into the world of data analytics, offering valuable insights into the role of data analysts in today's rapidly evolving business landscape.
Throughout the conversation, we dive deep into the tools and skills every aspiring data professional needs, including Microsoft Excel, Tableau, Power BI, SQL, AI, and more. Cristina also shares expert career advice, including strategies for excelling in job interviews, navigating career growth, and standing out in the competitive analytics field.
The How to Get an Analytics Job Podcast features discussions with top business analysts, data scientists, entrepreneurs, and business owners, focusing on the role of data in business decision-making, analytics job opportunities, and practical advice for career advancement. Whether you're just starting out in analytics or looking to elevate your career, the HTGAJ podcast is your go-to resource for actionable advice, career stories, and tips for success in data analytics.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 2 (00:12):
so we were talking off air.
So you graduated four years agofrom here years ago.
Yeah, all right, so walk us upto.
What are you up to right now?
Speaker 1 (00:23):
So currently I work as a senior data analyst at a
digital advertising company, andI've been there for about a
year and a half, almost.
Speaker 2 (00:32):
Okay, so you're doing marketing analytics essentially
.
Speaker 1 (00:35):
Yeah, essentially.
Speaker 2 (00:36):
Right, because it's interesting, because title kind
of doesn't matter.
Speaker 1 (00:40):
No.
Speaker 2 (00:41):
I was actually just talking with Molly, our mutual
friend about analysts has becomevery diluted.
Analyst is kind of a catch-allterm, but if you're a data
analyst for a marketing company,of course you're looking at
marketing data.
Speaker 1 (00:56):
Right.
Speaker 2 (00:57):
So walk me through that.
What kind of data sets are youlooking at?
What are the metrics?
How do you analyze that?
Speaker 1 (01:03):
Yeah, so there, um, one of the big things I work on
is what we call an mm, which isa media mix model um, because we
have media that we advertise onfrom like all sorts of
platforms like social media, umto print, to direct mail, to
email, um.
And then what we're trying todo with like mmms is like trying
(01:27):
to figure out how all of thoseinterplay um and how that
affects like conversions and youknow all the other kpis that a
company might be looking at um.
So the the data is is not supermessy, but obviously all data is
somewhat messy and then youhave to just find a way to bring
it all together and put it intoa model and get some results
(01:48):
that can tell the story of ofwhat your marketing dollars are
doing for you okay, so that'sinteresting.
Speaker 2 (01:55):
So you said model.
When I went like talking withyou I pictured you were doing
more like kind of like tableauor power bi dashboard work, that
too okay, so what's thedifference between the two?
Speaker 1 (02:07):
um.
So I would say I I'm kind of inlike a unique place right now
in my role in, where I um Istarted as a contractor to do
just dashboard visualizationsfor um, a specific tool in
salesforce.
It's very niche um and then Ikind of sought other
(02:27):
opportunities within that rolebecause they would kind of say
like, oh, we're working on thisand I would just kind of express
interest or express that I hadexperience in that and so other
teams would kind of pull me inwhen they needed help, because
you know, in most companies likeevery team gets overwhelmed at
some point and so I would kindof be like a floater and get
pulled into all these differentprojects just because I
(02:52):
sometimes I wasn't necessarilyan expert and I would just be
like, yeah, I'm really good atthis, even if I, even if I
wasn't, because I know I'm afast learner and I wanted the
experience.
Speaker 2 (03:02):
That's great.
So it sounds like over the lastfour years since you've been
working, you've gained quite abit of confidence.
Like you, you feel like you cango and learn a new like I mean,
are you?
What are you learning mostly,is it new kind of business use
cases or tools or some type ofcombination of the two?
Speaker 1 (03:21):
I think definitely like learning a lot of technical
skills along the way, a lot oflike with for, like, data
visualization, um, I think a lotof it like, yeah, sure, you can
take um like courses on it anddo all these things, but until
you're actually visualizing realdata you don't really know and
you're presenting it to peopleyou don't really know, like how
(03:43):
people are going to digest itand so you're like it to people
you don't really know, like howpeople are going to digest it.
Speaker 2 (03:50):
And so you're like actually doing something that is
impactful.
Yeah, having a use case makeslearning well.
I feel like what it does is itjust clarifies the path of like
okay, here's the output that Ineed, I'm getting paid for this.
If I don't deliver on this andthat happens, you know, maybe on
a repeating process I'm goingto lose my job.
So there's kind of like abaked-in pressure, and I feel
like the pressure is differentbecause when you were in my
(04:11):
class like I don't think, youwere like oh no, I need to solve
this problem or I'm not goingto pass the class.
It's a different dynamic, right, it's a different dynamic.
Speaker 1 (04:18):
I think when, like in class obviously, like I was
always someone who wanted to geta good grade right, um, so it
was like I did feel some sort ofpressure, but yeah, it's
obviously different in theworkplace, like it's a different
sort of pressure, um, I thinkin class you more, so it's like
easier to just kind of rely onlike more like textbook book
(04:41):
stuff or like stuff that youlearn in class.
There's hand-holding.
Yeah, it's like a lot there's alot of hand-holding.
Speaker 2 (04:44):
Yeah, it's like a lot .
There's a lot of hand-holding.
Speaker 1 (04:46):
There's like a rubric for things versus, like when
you're actually in the role,like you would think you would
get more direction, but youreally don't.
It's kind of like here's thedata.
Speaker 2 (04:56):
Right.
Well and that I think whatwe're kind of starting to circle
around as a topic was theconcept of critical thinking.
Like all across education theysay critical thinking, critical
thinking, but it's hard, it'sreally hard to incorporate that
into a class because, like, ifyou have a textbook and then you
have everything kind of likespoon fed into a structure,
(05:19):
that's not critical thinking.
That's right, that's just beingkind of obedient yeah, like
following directions ordetail-oriented yeah yeah yeah,
I think there's.
Speaker 1 (05:29):
There's a difference, like I know, like when doing
like the minor here withinbusiness analytics, um, we did a
lot of like stuff with realdata versus just like maybe in
other classes we would useliterally like it would.
The data would come from likethe digital textbook, and you
could download the file and thendo something with it and it
just it doesn't translate thatwell right when you're actually
(05:51):
doing work in the field.
Speaker 2 (05:53):
So one of the things that you said earlier on the
conversation I thought wasreally interesting and I wanted
to circle back around, was so,for the current role that you're
in you, you got hired as aconsultant or contractor and
then you started working onprojects that were slightly
outside of the scope of that.
Then they eventually reachedout to you and said hey, we want
(06:14):
to bring you on full time.
Speaker 1 (06:16):
Right.
Speaker 2 (06:17):
And I think what you've done is you've built up a
lot of like I don't even knowwhat to call it like business
capital or knowledge of what'sgoing on within their business.
So if they're trying to hire afull-time role, you've worked
for them for a year and you'vetouched multiple aspects of
their business you're going tostart out six months ahead of
(06:38):
somebody applying cold fromLinkedIn or Indeed.
So I think that was a reallyreally.
Did you do that intentionallyor were you just like?
I'm curious about this.
I want you know I'm bored atwork.
I want to.
I want to work on somethingelse, or here's an opportunity.
Speaker 1 (06:52):
Yeah, I think I just I saw the opportunity.
Somebody had reached out to mevia LinkedIn and it sounded
interesting and it was payingmore than what I was making at
my previous role and that,honestly, was one of the biggest
motivators for me.
And I was hesitant because Idid like my previous role, but
(07:13):
also I was like, okay, I'm superearly into my career.
It's not going to hurt me andalso I wasn't the ability to
contract, because obviously whenyou're contracting you don't
have those like full timebenefits.
So I was fortunate enough to bein a position where I could
take a contract role because Imy benefits were under like my
(07:36):
husband's, so like that alsokind of played into it as well.
Yeah, so you're like a prettystable yeah, because I
understand not everybody canjust like jump from a full
timetime to a contract, notknowing if they're going to get
hired full-time.
But doing that and like kind ofjust taking that leap of like,
I guess, like faith, it playedout well in the end.
Speaker 2 (07:55):
Well, it sounds like the contracting role was a big
opportunity but you reallymaximized it because not only
did you do that core jobfunction but you were starting
to do things kind of slightlyoutside of what was going on.
I guess the word that's kind ofpopping into my mind, which I'm
totally making up, is like youbuilt up quite a bit of career
(08:16):
equity within that year ofcontracting.
I would say so yeah, because theprojects that you're working on
now are they.
Are they starting to span, like, multiple aspects of the
business or For sure.
Speaker 1 (08:32):
Yeah, like especially , um, like the MMM work.
It's like slowly trying toexpand into other clients that
we have like kind of startedwith a few clients and now it's
like broadening to other clients, um to like pick up more
business, and things like that.
Speaker 2 (08:48):
That's awesome.
Okay, so this is a funny topicfor me.
I think you're probably moretechnically savvy than I am.
Like I'm, I'm 35, you're 25.
So I got 10 years on you 26.
Speaker 1 (09:02):
Okay.
Speaker 2 (09:03):
So I got nine years on you, but I feel like you know
more about technology and likethe like.
Aren't you coding in Python andstuff like that?
Speaker 1 (09:09):
Yeah, I do a little bit of Python coding.
Yeah, I would say like I've hadto pick up a lot more technical
skills, like in the role that Iam now, which is also something
that I wanted, because I'vealways been interested in
getting into those more liketechnical roles.
Speaker 2 (09:25):
So could you say some more about that, like what was
the first technical thing youever worked on?
And then how do you go aboutdeveloping those skills?
You just go online and likeGoogle or YouTube or whatever.
Speaker 1 (09:38):
Yeah, so I use a lot of chat GPT honestly to just
kind of like trial and errorstuff, I think.
Like the first, I can't reallyrecall.
Like the first like supertechnical thing, um, maybe just
like for dashboards using likejavascript and like html, okay,
(09:58):
and a lot of that I haven'ttouched either one of those.
Yeah, so I I did take like ajava j, like a JavaScript class
in college as part of my major,but outside of that I hadn't
like touched it.
And then, in the role when Iwas contracting, I had an
opportunity to do that.
Actually, one of my firstassignments was can you like
(10:19):
develop this in the dashboard?
And previously, like, peoplewere like, oh, oh, I don't think
it can be done, like itprobably can, but it just takes
like a lot of like manual codingin the back end.
Um, and I was able to figureout how to do it using like html
and javascript, even though,like, I hadn't used those tools
before, like in a in like a worksetting okay, so that was
(10:43):
within the salesforce dashboardwithin the salesforce dashboard
okay, because are you working intableau now?
because salesforce acquiredtableau right they did um, but I
I work with datarama, which issimilar to tableau.
In a way it's like a differentdatarama.
Yeah, that's what it's calledwell, Well, now it's called
(11:03):
sales.
It's had many names, but it itwas data Rama and now it's
technically Salesforce marketingintelligence.
Speaker 2 (11:13):
Okay, yeah, have you worked with Einstein?
Is Einstein's like the AIwithin sites?
Speaker 1 (11:19):
Yeah, we I get has a feature.
I don't really use that verymuch, um, but I mean I know it's
there, but lately I've beendoing more and getting slowly.
I actually just was put on adifferent team this past week
and I'll be starting on a newteam on Tuesday.
Um and I will be doing a lot ofpower BI work moving forward.
Speaker 2 (11:42):
Very cool.
Yeah, I haven't worked on PowerBI stuff in man.
It's been like eight months now.
Okay, I miss it.
Speaker 1 (11:49):
I know the first Power BI stuff I technically did
was like in your class, yeah.
And then I know those videotutorials of me doing stuff on
it are still on YouTubesomewhere.
Speaker 2 (12:01):
You had those big glasses.
Yeah, that's so funny, yeah,okay, so let's, let's talk about
the class.
Let's almost do like aretrospective of four years ago.
I, how did you find out aboutthe class?
I guess let's start there.
Speaker 1 (12:18):
I'm trying to remember.
I can't remember how I foundout about it, cause I was.
I mean, I was a math major,yeah, um, and then I my original
like trajectory was I wanted todo actuarial science right, I
remember that.
I wanted to be an actuary afterI graduated, because I wanted
to use math and it just madesense, I guess.
(12:39):
Um, and then it was, I think,before my senior or junior year,
I can't remember it had to beyour junior year.
It was junior yeah, it wasbefore my junior year, I think.
Um it just somebody announcedit about the analytics minor and
I just had um like space in mylike schedule, like enough
(13:02):
courses, um, because I wasalmost done with my math major,
like I technically could havegraduated, I think, a year or a
semester early, uh, but I choseto just do the analytics minor,
just to see you know what it wasabout right, well, I mean
actuarial and analytics.
Sounds like somewhat related orsimilar yeah, I guess I never
really got into the actuarialstuff and enough to really know
(13:25):
what it was about.
But I do know they use likeexcel a lot so okay.
Speaker 2 (13:31):
So then you showed up in my class and you're like who
is this guy?
Speaker 1 (13:34):
yeah, pretty much, because it was like it was your
class and then it was um, Ican't remember what the name of
the other classes were so it'scase studies and business
analytics and then the capstonecourse Actually.
Speaker 2 (13:48):
So I was consulting at the time and I believe you
got to work with Oliver Salesand Marketing, which was a
client of mine that I had forsix years, and yeah, actually I
mean I think we can talk aboutthat project a little bit.
I mean, don't get into the.
I was going to say, don't getinto the specifics.
It was four years ago, youdon't remember.
You don't remember the specifics, but yeah.
(14:08):
So what was the?
I guess let's start with thecase studies class.
So what did you kind of get outof out of that?
Speaker 1 (14:16):
I would say I got a lot out of it in terms of like
just skills that I could presentin an interview to, like you
know, present myself as likesomebody who could do a job.
Speaker 2 (14:30):
Because I believe even back then, because I've
changed, I've kind of iterated alittle bit, but I don't think I
gave you a final.
I think your final was aportfolio right, right, that's
correct.
So it's like you can literallysay here's the work that I've
done and yeah, I think thatreally helps you kind of stand
out in the marketplace.
Speaker 1 (14:50):
Yeah, just having like showing that you can do
stuff with the data.
Yeah, because prior to that,prior to those, to the analytics
minor, like obviously, like Ilearned a lot in my math courses
, stuff that I use today, likeyou know, just the math.
Speaker 2 (15:05):
A math degree is like a degree in problem solving
right.
Speaker 1 (15:09):
Yeah, I mean essentially yeah, I would say so
.
Yeah, it develops those problemsolving skills, but also it can
be very just like academia, youknow.
Speaker 2 (15:18):
Yeah.
Speaker 1 (15:19):
Like when you show up to an employer and you have a
math degree and you haven't donelike any um so it's like
theoretical versus practical,yeah theoretical is practical,
like obviously it's like.
Yeah you, I understand allthese like mathematical concepts
and all these things, but Ithink it wasn't until the
analytics minor where I hadsomething where I could go into
(15:39):
like a data analyst jobinterview and show them like I
can right do something that willbe of value to the company you
know.
Speaker 2 (15:49):
Right.
I mean, it's kind of like the,it's almost like the star
methodology.
It gives you like a veryconcrete situation, like a task,
a problem to solve, an actionyou took, and then the result
you know, or the recommendationGiving that framework, I think
is is really, really.
Yeah, I mean getting a mathdegree and an a minor in
(16:10):
analytics.
I feel like that's a greatcombination right there.
Yeah, I think it.
Speaker 1 (16:13):
I think it served me well so far.
Yeah, I think I, without it I'mnot really sure if I would have
been able to to really landlike my first job.
Honestly, just because, like itkind of was, the interview was
kind of like heavily on like oh,what projects have you done?
because obviously they knew Iwas straight out of college
right yeah, right, and so Ididn't have any job experience,
(16:37):
um, and so being able to talkabout like, oh, I worked on the
data for this company, it waslike who's the real company?
Right there's a real data set,gave them real actionable
results and got real feedbackfrom someone was like something
that I think made me stand out,probably from a lot of other
people coming out with a collegedegree oh yeah, especially like
(16:57):
entry-level people.
Speaker 2 (16:58):
You know, um, because I we actually talked about this
in class yesterday of going andapplying for like I have a
systematic way that I look atapplying and I have to kind of
remember and put myself backinto that situation of like
you're coming fresh out ofschool, you don't have it.
You might have like a part-timejob and you might have a few
(17:21):
projects under your belt, butit's not like I'm eight years
into my career at this point soI can apply to a wide range of
jobs.
Versus looking for thoseentry-level jobs, it just it
kind of thins the herd quite abit.
So, yeah, any type of like legup you can get I think is very
valuable.
Speaker 1 (17:40):
Yeah, for sure.
Speaker 2 (17:40):
So it is kind of cool that you, even while you're on
campus here, you got your firstexposure to marketing.
Speaker 1 (17:47):
Right.
Speaker 2 (17:48):
So I mean, I know this was four years ago, but,
like, what do you remember ofthat experience?
Speaker 1 (17:54):
I remember it being like a teamwork thing, yeah,
which I wasn't necessarily usedto in my other classes.
Speaker 2 (18:04):
I bet math is like pretty solitary.
Right, it's very solitary.
Speaker 1 (18:16):
Yeah, I guess, like the data and the output and then
kind of like just that dynamicof spreading the work and just
working in a team really waslike the thing that sticks out
to me, the most.
Speaker 2 (18:30):
That's interesting because I didn't think that
would be the big benefit.
I thought the big benefit wouldbe like you got to work with
the president of a pretty largecompany.
Yeah, that too of course, andit's like you're solving a
problem that hasn't been solvedbefore, like they're launching a
new, like sector of theirbusiness.
Yeah, and they don't.
There's no like, it's not.
Like you know, two plus twoequals, it's not.
(18:52):
I know that's just superkindergarten math.
Speaker 1 (18:54):
I know, but I get what you're saying.
Speaker 2 (18:55):
But there's no like right conclusion.
Speaker 1 (18:58):
Right.
Speaker 2 (18:58):
And I think that's what is really challenging to
teach a lot is the complexitiesand the assumptions you have to
make because, like you're nevergoing to have a perfect data set
Right, like you have to makebecause, like you're never going
to have a perfect data setright.
Like you know, right now I'mworking in the grocery business.
I mean, think about all of thepressures, like you know.
I mean we had that big um stormthat came through, like one of
(19:19):
our stores out in nashville iscompletely wiped out.
So like all the sales from that, um, but then also too, like
there was influence, uh, withinthe chicken.
So like the egg sales aredifferent, like there's so many
different things and that's askill set to itself of being
able to kind of cut through allthe noise and say, you know, to
(19:39):
the best of my ability or itcould be, I am 95% sure this is
the right answer.
Speaker 1 (19:44):
Right.
Speaker 2 (19:45):
Because I mean, within marketing the world that
you're doing now, like how, howdo you deal with ambiguity or
making assumptions?
Speaker 1 (19:53):
yeah, I mean there's obviously like a lot of outside
variables that affect, like anybuyer's decision, like you said,
in the grocery business, likeright, yeah literally never know
what's gonna happen.
Um, and yeah, I think inmarketing it's it's the same and
I think we saw that in thecapstone.
I think I remember, like all ofus having a lot of questions
about the data.
I do remember that, yeah,because we were all like
(20:16):
obviously unfamiliar with thebusiness as opposed to, maybe,
somebody who would be workingthere and like maybe more
familiar with it.
We were just like students withit.
We were just like students, um,but yeah, like I think in in
like the world of marketing nowit's like the way we deal with
it is kind of like using a lotof past data, um, right to kind
(20:38):
of just try to predict, in asense, and also to just
understand the data that we seethat comes in like on a
day-to-day or like a weekly ormonthly basis, um, but I mean,
you really just can't predict alot of things.
Like it is very unpredictablein every sector.
Speaker 2 (20:54):
Yeah, I mean, it's just something that I don't
think it's going to go away.
In your, in your career, You'regoing to have to deal with
ambiguity and having a classthat kind of introduces you to
that.
And you know, because you couldcome reach out to me and like I
could kind of help you.
At least I'm not very I don'tthink I'm very prescriptive, I'm
not like this is the rightanswer right, because there is
(21:16):
no right answer right, yeah,you're, you're just.
You're trying to do the best youcan with with whatever assets
or whatever you know resourcesyou have.
Yeah, at your disposal.
Speaker 1 (21:26):
That can be hard, I think, as a student, because you
know resources you have at yourdisposal.
That can be hard, I think, as astudent, because when you're a
student you're kind of most ofyour academic life is kind of
like okay, here's like the rightanswer, or like this is what a
good paper is if you need all ofthese things or check all these
boxes versus working with, likeactual company data.
And yeah, I think it was a good, a good thing to learn.
Speaker 2 (21:51):
Do you want to talk about the like transitioning
from a student to getting yourfirst job?
Speaker 1 (21:57):
Okay.
Speaker 2 (21:57):
Cause I remember you were like quite stressed about
that yeah, I mean, we don't haveto talk about that if you don't
want to no, it's okay, okay,all right.
So let's talk about thetransition from being a
full-time to working, becausethat was, I mean, that was a
pretty big challenge, right?
Speaker 1 (22:14):
Yeah, looking back at it, I think I was definitely
more stressed out than I neededto be, but also I'm somebody who
just like stresses abouteverything somebody who just
like stresses about everything.
Speaker 2 (22:30):
Well, I think you're used to like having things like
doing exceedingly well andhaving things like kind of go
well for you, right?
Speaker 1 (22:35):
I think there's yeah, like a lot of things have been
like if you do this, this willhappen right, like if you do
well in high school and you getgood grades and you'll get into
the college you want, or like beable to, you know, do x or y
things, um, and then going outand like try to find a job.
Speaker 2 (22:51):
It's like you're not guaranteed to find the role that
you want or the salary that youwant right, because I I think,
if I'm remembering correctly,you applied for like six weeks
and didn't hear anything backyeah and then all of a sudden,
it it.
You got multiple interviews inone week, right, right?
Speaker 1 (23:06):
yeah, so it was strange I would.
I would remember I would applyto a bunch of jobs and either
not hear anything back or justget like a rejection Right.
Speaker 2 (23:15):
Which both of those are demoralizing.
Yeah, they are.
Speaker 1 (23:17):
It's like very.
It's hard to constantly, youknow, feel like rejected.
And then I remember finallylike landing a couple interviews
and then some of them them likethey wouldn't disclose the pay
right away and sometimes the paywould just seem really low.
Even as somebody like entrylevel.
I would kind of think like, oh,that's like really low you know
(23:40):
, yeah, or somebody with what Ifelt were, you know, pretty
valuable skills.
Speaker 2 (23:46):
Yeah Well, I mean I don't want to come off as like
elitist or anything, but likeyou were coming into a market as
a skilled laborer.
You know it's not.
It's not like you were areplaceable cog, like there.
You know there there arespecific skills that you have
that not a lot of, you know it'snot a very high percentage of
the population has that.
Speaker 1 (24:06):
Yeah, I think now it's a little more saturated
especially, but I think backthen, like four years ago or
three years ago now.
Speaker 2 (24:15):
That's true.
I feel like you know, I've beenteaching for the past four or
five years, so I've kind of beenfollowing the market trends and
it was interesting because,yeah, it was a really good
market when you came out andthen it was good for another
year or two and then, I wouldsay, about a year or two ago,
(24:35):
the market just fell out.
There were a ton of peopletrying to get in and there just
were not very many entry-leveljobs.
That being said, the time thatyou you got in now it's like
these mid to senior level rolesare just wide open.
Yeah, so it's like it's reallycongested for entry level.
But then you go, you go up andalso too, like you've got assets
(24:58):
now, like you've got experienceworking.
You know what was?
What was that first role yougot?
Was it marketing?
it was also marketing yeah, solike you, you have a pretty deep
understanding of marketing atthis point, like you've been
working in it for years, so it'snot like I mean that that's.
Speaker 1 (25:12):
That is very, very valuable yeah, I think, yeah, I
think, when you are in the samelike space, um, you gain a lot
of like that business knowledgethat most people just don't have
.
Because if there's no real wayto get that knowledge unless
you're actually working in thespace, like I think, you could
even have like a marketingdegree and not actually know
(25:34):
what's going on at a digitalmedia agency well, it's the
theoretical knowledge versus thepractical knowledge, right?
Speaker 2 (25:40):
yeah, and it's funny because you were talking I don't
know why this just like kind ofsprung into my head, but you
were talking about, like,understanding business.
There's also another componentof like what you did in the
current role when you were goingand getting those other
projects.
There's organizationalknowledge too, so you know the
people.
You've built some type ofrelationship.
(26:01):
There's some trust, trustestablished, so so what,
essentially what you've done inin the role that you're, you've
you're in now is you've built alot of trust.
Have you ever kind of thoughtabout that?
There's value in that right.
Speaker 1 (26:15):
Yeah, and I'm realizing that, I think very
recently, I've realized that, asI've been working through
different teams within myorganization, as I've been
working through different teamswithin my organization, I think,
(26:37):
working with different projectmanagers, different heads of
teams, I've gotten to know mywork and have gotten to know
just, I guess, who I am as acolleague, that I am very
reliable and that I do delivergood work am very reliable and
that I do deliver um good work,and so I think word of that gets
around, um, and I think thatjust being somebody who is
willing to take on differenttypes of projects that are maybe
(26:58):
outside of my like, veryspecified role, um like puts me
in that situation yeah, and alsotoo is the term that's coming
to my head is like strangerdanger, like you're a known
entity to them, so like there's,there's a track record of like
okay, well, christina's donegood work, so it's accurate,
it's done on time, but she'salso pretty flexible.
Speaker 2 (27:20):
She's like open to um , you know, stretching a little
bit.
She's also a hard worker, youknow.
Like like that personalrelationship is is really
valuable and I feel like a lotof people, especially nowadays,
with like indeed, and linkedin,and I mean there's almost this
like tinder application of thejob market, like I applied to
(27:43):
196 jobs in two weeks when I waslooking back eight months ago.
Speaker 1 (27:46):
Oh, wow, okay.
Speaker 2 (27:47):
Because, like, I can go on and it takes five seconds
to do a one-click LinkedIn apply.
I mean, it's essentially likeswiping left or right on a
dating profile yeah, but there'sso much noise that happens
within that.
It's funny because Tinder cameout after I was an undergrad but
(28:09):
it seems like people are sofrustrated with, like you said,
you've got a friend that hatesthe apps.
Now that's single and it's justa mess.
That same kind of effect ishappening on the job market.
So, yeah, I just thought thatwas kind of cool that most
people don't really think aboutthat, like the equity that
(28:31):
you're building within theserelationships.
Speaker 1 (28:34):
Yeah, I think before I did also consider like that
whole like job hopping aspect ofthings, yeah, but also I think
when that was becoming trendy itwas a very different job market
, like right now people can'treally afford to go into, to
like just quit their job and gointo another one, because, like
you realistically never know ifthat role is going to be like
(28:58):
long-term, like they couldliterally cut you whenever if
they don't think you're a goodfit.
Like if you go into a new roleand they're like yeah, actually
they're really not a good fit,like they could just fire you if
they wanted to, versus like ifyou're, I mean obviously in my
(29:20):
role.
Speaker 2 (29:20):
They could fire me whenever they wanted to as well,
but I think there's obviouslymore of a risk in job hopping.
Um, yeah, but to push back onthat, like them, they could fire
you whenever they want.
But if you think about it,you've worked at the company
what?
for two years now a year and ahalf that they would be losing
all of the knowledge that you'vegained specifically within
their business and how it plugsinto the broader market.
You know what are the problemsthat they're facing and how you
(29:40):
can proactively solve them.
That like intuition that you'vedeveloped, Like that's valuable
to them and I think thatthey're aware of that.
So yeah, there's, but on theflip side of that, there's some
people who stay in jobs too longand it's bad.
So I feel like I've beenhearing a lot of one side of the
(30:03):
argument, not really kind oflike struggling with the nuance
of both.
Speaker 1 (30:08):
Yeah, I think definitely there's nuances to
both, cause I mean, you can stayin a role for a while, like,
obviously, if I'm in my role andthen, um, I don't get like the
pay increases that I feel like Ishould be getting, then yeah,
maybe that's a sign that they'renot valuing you as an employee
and that you shouldn't goelsewhere, right, um.
(30:28):
But I think if you can continueto grow and, like you know, if
you have growth opportunities,grow and, like you know, if you
have growth opportunities withinthe company, I think that's
different because obviously, ifyou're just like stagnant and
you're not learning anything newand you get kind of bored, then
yeah, maybe you should maybejob hop if you want.
Speaker 2 (30:48):
but yeah, and it's also so like individualized
right, like learn.
It sounds like one of your bigvalues is that you want to grow
and you want to learn.
But what if Susie Q over herejust had a child, right?
And has a young family Likethat's its own set of challenges
and learning, so beingchallenged there and then being
(31:08):
challenged on the job, so it'sno like one-size-fits-all
solution.
Speaker 1 (31:12):
Yeah, it's kind of like whatever you want, like
some people don't want to growin their role or grow and
they're like well, they valuestability right, they just want
to do what?
They've been doing and they'rethey're cool with it, which is
like that's cool right like.
I guess I'm someone who can getbored easily like I like to be
challenged right also I'm young,so I don't have, like all these
(31:35):
other major responsibilitiesoutside of, like my nine to five
.
Yeah, I don't have anyone tofeed.
Speaker 2 (31:43):
You got to feed yourself.
Speaker 1 (31:44):
Well yeah, besides myself obviously All right.
Speaker 2 (31:47):
So do you have any recommendations for, let's say,
someone's listening to thepodcast, that's like a sophomore
or junior in college right now.
Do you have any like advice orreflections that you would want
to pass on to them?
Speaker 1 (32:02):
I would say, to explore like courses that, um,
maybe are not, if you have theability to explore courses that
are outside of your major, justto see like what's out there, to
really try to get an idea of,like what you want to do after
(32:24):
college.
Because, like I thought, Ireally thought I wanted to be an
actuary and now, looking backat it, I don't think that's
something I could have done.
Speaker 2 (32:34):
Really Mm-hmm.
That is kind of cool becausewhen you were in my class you
kind of got your first exposureto real data, right, and it
sounds like I mean, obviouslyyou enjoyed it to some degree,
right.
Speaker 1 (32:49):
Yeah, I did.
Speaker 2 (32:53):
Very cool.
What about it?
Did you was the?
Because I remember when I firstgot exposure to like real world
data, it stressed me out.
I was like this this is notformatted correctly, it's not
accurate, there's missing data.
How to clean that up?
But it sounds like with withyour experience of, it was like
(33:13):
oh, this is a challenge yeah andthis is new, and this is like I
don't know.
Speaker 1 (33:19):
You're solving a problem that's not been
specifically solved before yeah,I think it's just like being
able to, because I think there'sa lot of like creativity when
it comes to like looking at data, um, that a lot of people don't
necessarily think because it'slike oh, it's just like
analytics, like very liketechnical, but I think there's a
lot of creativity that goesinto like looking at the data
(33:41):
and trying to, like you know,gain insights from it, because
you do have to look at it fromlike all sorts of angles.
And even just the data cleaningprocess itself, which is kind of
like the lengthiest processmost of the time.
I think you could learn a lotabout the data cleaning process
itself, which is kind of likethe lengthiest process most of
the time.
I think you could learn a lotabout the data with just like
cleaning it, um, and I don'tknow, I think it's just that
iterative process of like okay,here's the data, let's clean it,
(34:04):
and then let's like visualizeit and see what we can gain from
it.
Like here are the questionsthat we have as a business.
Just that there's like a lot ofum, there's a lot of freedom to
to solving like problems whenit comes to data yeah, that's
true and the position I'm in now.
Speaker 2 (34:24):
There's multiple ways that you could solve the
problem, but that is actuallysecondary.
What the main thing is.
It's almost like being a lawyerin court You've got to be able
to justify what you're puttingout there.
Because, they're not alwaysgoing to say, hey, this seems
off, but they could at any time.
So you've kind of got to beready to justify whatever
(34:46):
recommendation you're making.
Speaker 1 (34:47):
Yeah, and I think you have to be ready also to look
back at your work and be like oh, maybe I did make a mistake,
Maybe I did look at somethingincorrectly or maybe I'm not
looking at it through the properlens.
Speaker 2 (35:00):
The right lens yeah.
Speaker 1 (35:01):
And that's happened to me a lot at work, and
sometimes there's people in theorganization who have experience
and knowledge on a certainaspect or a certain channel,
like in marketing, like directmail or print.
I don't really know much aboutthose things.
Speaker 2 (35:20):
Those are like old school marketing, right.
Speaker 1 (35:22):
Yeah, exactly, that's like the old school marketing.
And so somebody might have alot of knowledge on that that I
just simply don't, because Ijust don't know much about it.
And so it's like if I'mpresenting results and they're
looking at it like oh, that thatdoesn't seem right or like are
you, you know, can you tell memore about this result?
And if I can't really say muchabout it, then it's like okay,
(35:43):
maybe I have to go back andreally question what the output
is from this data set.
And right, you know, maybebring somebody else in to be
like hey, hey, what do you thinkabout this?
Speaker 2 (35:55):
So I've got a question for you, because I'm
one of these Are you more of abig picture thinker or more of a
detail person?
Have you been asked this before?
Speaker 1 (36:08):
I don't think I have.
Speaker 2 (36:09):
Oh, that's a good question.
Speaker 1 (36:10):
I'm sure I feel like I'm.
I'm probably more, I would say,like the small.
Speaker 2 (36:20):
I was gonna say my, my understanding of you, or like
at least what I've seen.
You seem very detail, detailoriented yeah, to where.
I'm much more of a bigger like.
I'm much more interested in thestrategy aspect of it okay and
like less the kind of innerworkings of like the algorithm
I'm like is this right or themodel?
Speaker 1 (36:40):
that's being built Right right.
Like you, like kind of buildingthe model right yeah, and I get
into the nitty-gritty of likewhat the model is doing.
Speaker 2 (36:47):
I would like for someone to kind of build that
out and then I can use it tothen drive a business decision
and then I like quantifying that.
That's kind of like where Ilike to sit on things, okay,
which kind of makes sense.
Now, as to why you're goingmuch deeper into the technical
(37:07):
stuff, because, like you, like Imean, I wonder, it seems like
we're just kind of wireddifferently, huh.
Maybe, Like well, because, likedo you remember why you thought
it was a good idea to get a mathdegree?
Because from my perspective,like that seems like pulling
teeth.
Speaker 1 (37:26):
Because I really like math.
Speaker 2 (37:27):
Really.
Speaker 1 (37:28):
Yeah, that was just my thing.
I was like okay, I really likemath and I'm good at it, so let
me just my thing.
I was like okay, I really likemath and I'm good at it, so let
me just do that.
Speaker 2 (37:36):
See, I can remember in high school having a moment
where I was like I might havebeen trigonometry or something.
I was like I don't like this.
I don't know if I can gofurther with this.
Speaker 1 (37:47):
Yeah.
Speaker 2 (37:48):
Like I don't know, math felt overwhelming to me,
especially when I was younger,but it sounds like your exposure
to it.
Speaker 1 (37:54):
Yeah, I've always liked it.
Speaker 2 (37:55):
It was like more of like a curiosity-inducing for
you.
Speaker 1 (37:59):
Yeah, I thought it was I don't know, I just thought
it was cool, like I just reallyliked math always.
Speaker 2 (38:05):
Okay.
Speaker 1 (38:06):
Just like really nerdy.
Speaker 2 (38:07):
What's cool about like just the fact that it's
logic or fact-based?
Speaker 1 (38:14):
Yeah, and I think it's just like.
I mean, I really like the waythat it can explain or that it I
guess it like parallels, likenature in so many ways, like
that's one of the mostinteresting things about math to
me, like the.
Fibonacci sequences, like youknow, like the number 108 of the
universe, just things like thatalways fascinated me and I
(38:37):
think obviously when you'reyounger you can't understand the
actual mathematical concepts ofthem.
When you get into college youcan begin to really understand
these things.
I don't know, I just thought itwas really interesting and a lot
of things kind of just clickedfor me.
You know, begin to reallyunderstand these things, and I
don't know, I just thought itwas really interesting and it
(38:57):
just a lot of things kind ofjust clicked for me that maybe
not everyone else like just getsit as easily, just because they
probably don't have an interestin it.
I think it takes an interestfor it to be easier to learn.
Speaker 2 (39:09):
Yeah, Well, I mean, I think it's just interesting
that, like we're both in kind ofthe analytics, you know, space,
but we're very, two different,two very different personality
types.
Because I'm much moreinterested in like big picture,
you know.
I mean I used to joke like whenI was running silverton
analytics, like a lot of thedata visualization work, I would
(39:31):
be like this is bigger thanthis, so you should make this
decision.
It's like basically makingpicture books for influential
adults, like if I was being alittle cutesy about like saying
what I, what?
Speaker 1 (39:42):
I do yeah.
Speaker 2 (39:43):
But you're like no, I want to like, build out the
algorithm and get into.
Ai and all this crazy stuff.
Speaker 1 (39:49):
Yeah.
Speaker 2 (39:50):
But I mean, I think just being aware of that is is
valuable in that you can startto inform like, like what's next
, like what, what?
I mean?
Are you thinking about gettingto like more into, like data
science?
Speaker 1 (40:04):
um, I think my next step, uh, that I've been
considering this year, I wantedto get a master's, okay, yeah,
so I'm considering a master's inai oh wow, that's really cool I
might do that.
Speaker 2 (40:18):
I might apply in june so tell me more about is what
man.
I'm gonna sound like a boomerhere.
Well, what is chat?
Gpt?
Is that ai?
Yes okay, yeah, but and what isit doing exactly?
I mean, I sound so dumb rightnow.
Speaker 1 (40:34):
Yeah, I mean I guess it's just.
I mean, at the end of the day,it is kind of just like a
chatbot, because it's beingtrained on what input you're
giving it, but also like,obviously, when they built it it
was trained on like all thisdata that we have.
Speaker 2 (40:51):
So is it like a?
It's just like machine learning.
It's like a series of nestedifs and statements.
Is that what you're saying?
Speaker 1 (40:57):
Sure.
Speaker 2 (40:57):
Isn't that what the joke is right?
Speaker 1 (40:58):
now yeah, no, I mean it goes a lot deeper than that.
But yeah, I mean, it's just.
Speaker 2 (41:06):
Very cool.
Speaker 1 (41:07):
Kind of like an advanced chatbot.
Speaker 2 (41:14):
Have you been on any of the episodes with michael
galarnik?
I think, so so he's studyingmachine learning for finance,
okay, at georgia tech.
So he I need to connect youwith him because he would be
somebody.
So are you thinking aboutgetting a master's or he's he's
doing?
a master's, I don't think Icould do the yeah, well, because
like that is, but getting a phdin a, in like a practical space
, like that, I think it's areally interesting concept.
(41:36):
Yeah, because you could teach,but then you could also go work
for you know some type oftrading company and build them
an algorithm that's worthmillions upon millions of
dollars.
Speaker 1 (41:47):
I guess yeah, it's always an option but alright,
cool.
Speaker 2 (41:52):
Well, christina, thank you for for joining us.
This has been really fun.
Speaker 1 (41:56):
Thanks for having me, it's been great.
so we were talking off air.
So you graduated four years agofrom here years ago.
Yeah, all right, so walk us upto.
What are you up to right now?
Speaker 1 (00:23):
So currently I work as a senior data analyst at a
digital advertising company, andI've been there for about a
year and a half, almost.
Speaker 2 (00:32):
Okay, so you're doing marketing analytics essentially
.
Speaker 1 (00:35):
Yeah, essentially.
Speaker 2 (00:36):
Right, because it's interesting, because title kind
of doesn't matter.
Speaker 1 (00:40):
No.
Speaker 2 (00:41):
I was actually just talking with Molly, our mutual
friend about analysts has becomevery diluted.
Analyst is kind of a catch-allterm, but if you're a data
analyst for a marketing company,of course you're looking at
marketing data.
Speaker 1 (00:56):
Right.
Speaker 2 (00:57):
So walk me through that.
What kind of data sets are youlooking at?
What are the metrics?
How do you analyze that?
Speaker 1 (01:03):
Yeah, so there, um, one of the big things I work on
is what we call an mm, which isa media mix model um, because we
have media that we advertise onfrom like all sorts of
platforms like social media, umto print, to direct mail, to
email, um.
And then what we're trying todo with like mmms is like trying
(01:27):
to figure out how all of thoseinterplay um and how that
affects like conversions and youknow all the other kpis that a
company might be looking at um.
So the the data is is not supermessy, but obviously all data is
somewhat messy and then youhave to just find a way to bring
it all together and put it intoa model and get some results
(01:48):
that can tell the story of ofwhat your marketing dollars are
doing for you okay, so that'sinteresting.
Speaker 2 (01:55):
So you said model.
When I went like talking withyou I pictured you were doing
more like kind of like tableauor power bi dashboard work, that
too okay, so what's thedifference between the two?
Speaker 1 (02:07):
um.
So I would say I I'm kind of inlike a unique place right now
in my role in, where I um Istarted as a contractor to do
just dashboard visualizationsfor um, a specific tool in
salesforce.
It's very niche um and then Ikind of sought other
(02:27):
opportunities within that rolebecause they would kind of say
like, oh, we're working on thisand I would just kind of express
interest or express that I hadexperience in that and so other
teams would kind of pull me inwhen they needed help, because
you know, in most companies likeevery team gets overwhelmed at
some point and so I would kindof be like a floater and get
pulled into all these differentprojects just because I
(02:52):
sometimes I wasn't necessarilyan expert and I would just be
like, yeah, I'm really good atthis, even if I, even if I
wasn't, because I know I'm afast learner and I wanted the
experience.
Speaker 2 (03:02):
That's great.
So it sounds like over the lastfour years since you've been
working, you've gained quite abit of confidence.
Like you, you feel like you cango and learn a new like I mean,
are you?
What are you learning mostly,is it new kind of business use
cases or tools or some type ofcombination of the two?
Speaker 1 (03:21):
I think definitely like learning a lot of technical
skills along the way, a lot oflike with for, like, data
visualization, um, I think a lotof it like, yeah, sure, you can
take um like courses on it anddo all these things, but until
you're actually visualizing realdata you don't really know and
you're presenting it to peopleyou don't really know, like how
(03:43):
people are going to digest itand so you're like it to people
you don't really know, like howpeople are going to digest it.
Speaker 2 (03:50):
And so you're like actually doing something that is
impactful.
Yeah, having a use case makeslearning well.
I feel like what it does is itjust clarifies the path of like
okay, here's the output that Ineed, I'm getting paid for this.
If I don't deliver on this andthat happens, you know, maybe on
a repeating process I'm goingto lose my job.
So there's kind of like abaked-in pressure, and I feel
like the pressure is differentbecause when you were in my
(04:11):
class like I don't think, youwere like oh no, I need to solve
this problem or I'm not goingto pass the class.
It's a different dynamic, right, it's a different dynamic.
Speaker 1 (04:18):
I think when, like in class obviously, like I was
always someone who wanted to geta good grade right, um, so it
was like I did feel some sort ofpressure, but yeah, it's
obviously different in theworkplace, like it's a different
sort of pressure, um, I thinkin class you more, so it's like
easier to just kind of rely onlike more like textbook book
(04:41):
stuff or like stuff that youlearn in class.
There's hand-holding.
Yeah, it's like a lot there's alot of hand-holding.
Speaker 2 (04:44):
Yeah, it's like a lot .
There's a lot of hand-holding.
Speaker 1 (04:46):
There's like a rubric for things versus, like when
you're actually in the role,like you would think you would
get more direction, but youreally don't.
It's kind of like here's thedata.
Speaker 2 (04:56):
Right.
Well and that I think whatwe're kind of starting to circle
around as a topic was theconcept of critical thinking.
Like all across education theysay critical thinking, critical
thinking, but it's hard, it'sreally hard to incorporate that
into a class because, like, ifyou have a textbook and then you
have everything kind of likespoon fed into a structure,
(05:19):
that's not critical thinking.
That's right, that's just beingkind of obedient yeah, like
following directions ordetail-oriented yeah yeah yeah,
I think there's.
Speaker 1 (05:29):
There's a difference, like I know, like when doing
like the minor here withinbusiness analytics, um, we did a
lot of like stuff with realdata versus just like maybe in
other classes we would useliterally like it would.
The data would come from likethe digital textbook, and you
could download the file and thendo something with it and it
just it doesn't translate thatwell right when you're actually
(05:51):
doing work in the field.
Speaker 2 (05:53):
So one of the things that you said earlier on the
conversation I thought wasreally interesting and I wanted
to circle back around, was so,for the current role that you're
in you, you got hired as aconsultant or contractor and
then you started working onprojects that were slightly
outside of the scope of that.
Then they eventually reachedout to you and said hey, we want
(06:14):
to bring you on full time.
Speaker 1 (06:16):
Right.
Speaker 2 (06:17):
And I think what you've done is you've built up a
lot of like I don't even knowwhat to call it like business
capital or knowledge of what'sgoing on within their business.
So if they're trying to hire afull-time role, you've worked
for them for a year and you'vetouched multiple aspects of
their business you're going tostart out six months ahead of
(06:38):
somebody applying cold fromLinkedIn or Indeed.
So I think that was a reallyreally.
Did you do that intentionallyor were you just like?
I'm curious about this.
I want you know I'm bored atwork.
I want to.
I want to work on somethingelse, or here's an opportunity.
Speaker 1 (06:52):
Yeah, I think I just I saw the opportunity.
Somebody had reached out to mevia LinkedIn and it sounded
interesting and it was payingmore than what I was making at
my previous role and that,honestly, was one of the biggest
motivators for me.
And I was hesitant because Idid like my previous role, but
(07:13):
also I was like, okay, I'm superearly into my career.
It's not going to hurt me andalso I wasn't the ability to
contract, because obviously whenyou're contracting you don't
have those like full timebenefits.
So I was fortunate enough to bein a position where I could
take a contract role because Imy benefits were under like my
(07:36):
husband's, so like that alsokind of played into it as well.
Yeah, so you're like a prettystable yeah, because I
understand not everybody canjust like jump from a full
timetime to a contract, notknowing if they're going to get
hired full-time.
But doing that and like kind ofjust taking that leap of like,
I guess, like faith, it playedout well in the end.
Speaker 2 (07:55):
Well, it sounds like the contracting role was a big
opportunity but you reallymaximized it because not only
did you do that core jobfunction but you were starting
to do things kind of slightlyoutside of what was going on.
I guess the word that's kind ofpopping into my mind, which I'm
totally making up, is like youbuilt up quite a bit of career
(08:16):
equity within that year ofcontracting.
I would say so yeah, because theprojects that you're working on
now are they.
Are they starting to span, like, multiple aspects of the
business or For sure.
Speaker 1 (08:32):
Yeah, like especially , um, like the MMM work.
It's like slowly trying toexpand into other clients that
we have like kind of startedwith a few clients and now it's
like broadening to other clients, um to like pick up more
business, and things like that.
Speaker 2 (08:48):
That's awesome.
Okay, so this is a funny topicfor me.
I think you're probably moretechnically savvy than I am.
Like I'm, I'm 35, you're 25.
So I got 10 years on you 26.
Speaker 1 (09:02):
Okay.
Speaker 2 (09:03):
So I got nine years on you, but I feel like you know
more about technology and likethe like.
Aren't you coding in Python andstuff like that?
Speaker 1 (09:09):
Yeah, I do a little bit of Python coding.
Yeah, I would say like I've hadto pick up a lot more technical
skills, like in the role that Iam now, which is also something
that I wanted, because I'vealways been interested in
getting into those more liketechnical roles.
Speaker 2 (09:25):
So could you say some more about that, like what was
the first technical thing youever worked on?
And then how do you go aboutdeveloping those skills?
You just go online and likeGoogle or YouTube or whatever.
Speaker 1 (09:38):
Yeah, so I use a lot of chat GPT honestly to just
kind of like trial and errorstuff, I think.
Like the first, I can't reallyrecall.
Like the first like supertechnical thing, um, maybe just
like for dashboards using likejavascript and like html, okay,
(09:58):
and a lot of that I haven'ttouched either one of those.
Yeah, so I I did take like ajava j, like a JavaScript class
in college as part of my major,but outside of that I hadn't
like touched it.
And then, in the role when Iwas contracting, I had an
opportunity to do that.
Actually, one of my firstassignments was can you like
(10:19):
develop this in the dashboard?
And previously, like, peoplewere like, oh, oh, I don't think
it can be done, like itprobably can, but it just takes
like a lot of like manual codingin the back end.
Um, and I was able to figureout how to do it using like html
and javascript, even though,like, I hadn't used those tools
before, like in a in like a worksetting okay, so that was
(10:43):
within the salesforce dashboardwithin the salesforce dashboard
okay, because are you working intableau now?
because salesforce acquiredtableau right they did um, but I
I work with datarama, which issimilar to tableau.
In a way it's like a differentdatarama.
Yeah, that's what it's calledwell, Well, now it's called
(11:03):
sales.
It's had many names, but it itwas data Rama and now it's
technically Salesforce marketingintelligence.
Speaker 2 (11:13):
Okay, yeah, have you worked with Einstein?
Is Einstein's like the AIwithin sites?
Speaker 1 (11:19):
Yeah, we I get has a feature.
I don't really use that verymuch, um, but I mean I know it's
there, but lately I've beendoing more and getting slowly.
I actually just was put on adifferent team this past week
and I'll be starting on a newteam on Tuesday.
Um and I will be doing a lot ofpower BI work moving forward.
Speaker 2 (11:42):
Very cool.
Yeah, I haven't worked on PowerBI stuff in man.
It's been like eight months now.
Okay, I miss it.
Speaker 1 (11:49):
I know the first Power BI stuff I technically did
was like in your class, yeah.
And then I know those videotutorials of me doing stuff on
it are still on YouTubesomewhere.
Speaker 2 (12:01):
You had those big glasses.
Yeah, that's so funny, yeah,okay, so let's, let's talk about
the class.
Let's almost do like aretrospective of four years ago.
I, how did you find out aboutthe class?
I guess let's start there.
Speaker 1 (12:18):
I'm trying to remember.
I can't remember how I foundout about it, cause I was.
I mean, I was a math major,yeah, um, and then I my original
like trajectory was I wanted todo actuarial science right, I
remember that.
I wanted to be an actuary afterI graduated, because I wanted
to use math and it just madesense, I guess.
(12:39):
Um, and then it was, I think,before my senior or junior year,
I can't remember it had to beyour junior year.
It was junior yeah, it wasbefore my junior year, I think.
Um it just somebody announcedit about the analytics minor and
I just had um like space in mylike schedule, like enough
(13:02):
courses, um, because I wasalmost done with my math major,
like I technically could havegraduated, I think, a year or a
semester early, uh, but I choseto just do the analytics minor,
just to see you know what it wasabout right, well, I mean
actuarial and analytics.
Sounds like somewhat related orsimilar yeah, I guess I never
really got into the actuarialstuff and enough to really know
(13:25):
what it was about.
But I do know they use likeexcel a lot so okay.
Speaker 2 (13:31):
So then you showed up in my class and you're like who
is this guy?
Speaker 1 (13:34):
yeah, pretty much, because it was like it was your
class and then it was um, Ican't remember what the name of
the other classes were so it'scase studies and business
analytics and then the capstonecourse Actually.
Speaker 2 (13:48):
So I was consulting at the time and I believe you
got to work with Oliver Salesand Marketing, which was a
client of mine that I had forsix years, and yeah, actually I
mean I think we can talk aboutthat project a little bit.
I mean, don't get into the.
I was going to say, don't getinto the specifics.
It was four years ago, youdon't remember.
You don't remember the specifics, but yeah.
(14:08):
So what was the?
I guess let's start with thecase studies class.
So what did you kind of get outof out of that?
Speaker 1 (14:16):
I would say I got a lot out of it in terms of like
just skills that I could presentin an interview to, like you
know, present myself as likesomebody who could do a job.
Speaker 2 (14:30):
Because I believe even back then, because I've
changed, I've kind of iterated alittle bit, but I don't think I
gave you a final.
I think your final was aportfolio right, right, that's
correct.
So it's like you can literallysay here's the work that I've
done and yeah, I think thatreally helps you kind of stand
out in the marketplace.
Speaker 1 (14:50):
Yeah, just having like showing that you can do
stuff with the data.
Yeah, because prior to that,prior to those, to the analytics
minor, like obviously, like Ilearned a lot in my math courses
, stuff that I use today, likeyou know, just the math.
Speaker 2 (15:05):
A math degree is like a degree in problem solving
right.
Speaker 1 (15:09):
Yeah, I mean essentially yeah, I would say so
.
Yeah, it develops those problemsolving skills, but also it can
be very just like academia, youknow.
Speaker 2 (15:18):
Yeah.
Speaker 1 (15:19):
Like when you show up to an employer and you have a
math degree and you haven't donelike any um so it's like
theoretical versus practical,yeah theoretical is practical,
like obviously it's like.
Yeah you, I understand allthese like mathematical concepts
and all these things, but Ithink it wasn't until the
analytics minor where I hadsomething where I could go into
(15:39):
like a data analyst jobinterview and show them like I
can right do something that willbe of value to the company you
know.
Speaker 2 (15:49):
Right.
I mean, it's kind of like the,it's almost like the star
methodology.
It gives you like a veryconcrete situation, like a task,
a problem to solve, an actionyou took, and then the result
you know, or the recommendationGiving that framework, I think
is is really, really.
Yeah, I mean getting a mathdegree and an a minor in
(16:10):
analytics.
I feel like that's a greatcombination right there.
Yeah, I think it.
Speaker 1 (16:13):
I think it served me well so far.
Yeah, I think I, without it I'mnot really sure if I would have
been able to to really landlike my first job.
Honestly, just because, like itkind of was, the interview was
kind of like heavily on like oh,what projects have you done?
because obviously they knew Iwas straight out of college
right yeah, right, and so Ididn't have any job experience,
(16:37):
um, and so being able to talkabout like, oh, I worked on the
data for this company, it waslike who's the real company?
Right there's a real data set,gave them real actionable
results and got real feedbackfrom someone was like something
that I think made me stand out,probably from a lot of other
people coming out with a collegedegree oh yeah, especially like
(16:57):
entry-level people.
Speaker 2 (16:58):
You know, um, because I we actually talked about this
in class yesterday of going andapplying for like I have a
systematic way that I look atapplying and I have to kind of
remember and put myself backinto that situation of like
you're coming fresh out ofschool, you don't have it.
You might have like a part-timejob and you might have a few
(17:21):
projects under your belt, butit's not like I'm eight years
into my career at this point soI can apply to a wide range of
jobs.
Versus looking for thoseentry-level jobs, it just it
kind of thins the herd quite abit.
So, yeah, any type of like legup you can get I think is very
valuable.
Speaker 1 (17:40):
Yeah, for sure.
Speaker 2 (17:40):
So it is kind of cool that you, even while you're on
campus here, you got your firstexposure to marketing.
Speaker 1 (17:47):
Right.
Speaker 2 (17:48):
So I mean, I know this was four years ago, but,
like, what do you remember ofthat experience?
Speaker 1 (17:54):
I remember it being like a teamwork thing, yeah,
which I wasn't necessarily usedto in my other classes.
Speaker 2 (18:04):
I bet math is like pretty solitary.
Right, it's very solitary.
Speaker 1 (18:16):
Yeah, I guess, like the data and the output and then
kind of like just that dynamicof spreading the work and just
working in a team really waslike the thing that sticks out
to me, the most.
Speaker 2 (18:30):
That's interesting because I didn't think that
would be the big benefit.
I thought the big benefit wouldbe like you got to work with
the president of a pretty largecompany.
Yeah, that too of course, andit's like you're solving a
problem that hasn't been solvedbefore, like they're launching a
new, like sector of theirbusiness.
Yeah, and they don't.
There's no like, it's not.
Like you know, two plus twoequals, it's not.
(18:52):
I know that's just superkindergarten math.
Speaker 1 (18:54):
I know, but I get what you're saying.
Speaker 2 (18:55):
But there's no like right conclusion.
Speaker 1 (18:58):
Right.
Speaker 2 (18:58):
And I think that's what is really challenging to
teach a lot is the complexitiesand the assumptions you have to
make because, like you're nevergoing to have a perfect data set
Right, like you have to makebecause, like you're never going
to have a perfect data setright.
Like you know, right now I'mworking in the grocery business.
I mean, think about all of thepressures, like you know.
I mean we had that big um stormthat came through, like one of
(19:19):
our stores out in nashville iscompletely wiped out.
So like all the sales from that, um, but then also too, like
there was influence, uh, withinthe chicken.
So like the egg sales aredifferent, like there's so many
different things and that's askill set to itself of being
able to kind of cut through allthe noise and say, you know, to
(19:39):
the best of my ability or itcould be, I am 95% sure this is
the right answer.
Speaker 1 (19:44):
Right.
Speaker 2 (19:45):
Because I mean, within marketing the world that
you're doing now, like how, howdo you deal with ambiguity or
making assumptions?
Speaker 1 (19:53):
yeah, I mean there's obviously like a lot of outside
variables that affect, like anybuyer's decision, like you said,
in the grocery business, likeright, yeah literally never know
what's gonna happen.
Um, and yeah, I think inmarketing it's it's the same and
I think we saw that in thecapstone.
I think I remember, like all ofus having a lot of questions
about the data.
I do remember that, yeah,because we were all like
(20:16):
obviously unfamiliar with thebusiness as opposed to, maybe,
somebody who would be workingthere and like maybe more
familiar with it.
We were just like students withit.
We were just like students, um,but yeah, like I think in in
like the world of marketing nowit's like the way we deal with
it is kind of like using a lotof past data, um, right to kind
(20:38):
of just try to predict, in asense, and also to just
understand the data that we seethat comes in like on a
day-to-day or like a weekly ormonthly basis, um, but I mean,
you really just can't predict alot of things.
Like it is very unpredictablein every sector.
Speaker 2 (20:54):
Yeah, I mean, it's just something that I don't
think it's going to go away.
In your, in your career, You'regoing to have to deal with
ambiguity and having a classthat kind of introduces you to
that.
And you know, because you couldcome reach out to me and like I
could kind of help you.
At least I'm not very I don'tthink I'm very prescriptive, I'm
not like this is the rightanswer right, because there is
(21:16):
no right answer right, yeah,you're, you're just.
You're trying to do the best youcan with with whatever assets
or whatever you know resourcesyou have.
Yeah, at your disposal.
Speaker 1 (21:26):
That can be hard, I think, as a student, because you
know resources you have at yourdisposal.
That can be hard, I think, as astudent, because when you're a
student you're kind of most ofyour academic life is kind of
like okay, here's like the rightanswer, or like this is what a
good paper is if you need all ofthese things or check all these
boxes versus working with, likeactual company data.
And yeah, I think it was a good, a good thing to learn.
Speaker 2 (21:51):
Do you want to talk about the like transitioning
from a student to getting yourfirst job?
Speaker 1 (21:57):
Okay.
Speaker 2 (21:57):
Cause I remember you were like quite stressed about
that yeah, I mean, we don't haveto talk about that if you don't
want to no, it's okay, okay,all right.
So let's talk about thetransition from being a
full-time to working, becausethat was, I mean, that was a
pretty big challenge, right?
Speaker 1 (22:14):
Yeah, looking back at it, I think I was definitely
more stressed out than I neededto be, but also I'm somebody who
just like stresses abouteverything somebody who just
like stresses about everything.
Speaker 2 (22:30):
Well, I think you're used to like having things like
doing exceedingly well andhaving things like kind of go
well for you, right?
Speaker 1 (22:35):
I think there's yeah, like a lot of things have been
like if you do this, this willhappen right, like if you do
well in high school and you getgood grades and you'll get into
the college you want, or like beable to, you know, do x or y
things, um, and then going outand like try to find a job.
Speaker 2 (22:51):
It's like you're not guaranteed to find the role that
you want or the salary that youwant right, because I I think,
if I'm remembering correctly,you applied for like six weeks
and didn't hear anything backyeah and then all of a sudden,
it it.
You got multiple interviews inone week, right, right?
Speaker 1 (23:06):
yeah, so it was strange I would.
I would remember I would applyto a bunch of jobs and either
not hear anything back or justget like a rejection Right.
Speaker 2 (23:15):
Which both of those are demoralizing.
Yeah, they are.
Speaker 1 (23:17):
It's like very.
It's hard to constantly, youknow, feel like rejected.
And then I remember finallylike landing a couple interviews
and then some of them them likethey wouldn't disclose the pay
right away and sometimes the paywould just seem really low.
Even as somebody like entrylevel.
I would kind of think like, oh,that's like really low you know
(23:40):
, yeah, or somebody with what Ifelt were, you know, pretty
valuable skills.
Speaker 2 (23:46):
Yeah Well, I mean I don't want to come off as like
elitist or anything, but likeyou were coming into a market as
a skilled laborer.
You know it's not.
It's not like you were areplaceable cog, like there.
You know there there arespecific skills that you have
that not a lot of, you know it'snot a very high percentage of
the population has that.
Speaker 1 (24:06):
Yeah, I think now it's a little more saturated
especially, but I think backthen, like four years ago or
three years ago now.
Speaker 2 (24:15):
That's true.
I feel like you know, I've beenteaching for the past four or
five years, so I've kind of beenfollowing the market trends and
it was interesting because,yeah, it was a really good
market when you came out andthen it was good for another
year or two and then, I wouldsay, about a year or two ago,
(24:35):
the market just fell out.
There were a ton of peopletrying to get in and there just
were not very many entry-leveljobs.
That being said, the time thatyou you got in now it's like
these mid to senior level rolesare just wide open.
Yeah, so it's like it's reallycongested for entry level.
But then you go, you go up andalso too, like you've got assets
(24:58):
now, like you've got experienceworking.
You know what was?
What was that first role yougot?
Was it marketing?
it was also marketing yeah, solike you, you have a pretty deep
understanding of marketing atthis point, like you've been
working in it for years, so it'snot like I mean that that's.
Speaker 1 (25:12):
That is very, very valuable yeah, I think, yeah, I
think, when you are in the samelike space, um, you gain a lot
of like that business knowledgethat most people just don't have
.
Because if there's no real wayto get that knowledge unless
you're actually working in thespace, like I think, you could
even have like a marketingdegree and not actually know
(25:34):
what's going on at a digitalmedia agency well, it's the
theoretical knowledge versus thepractical knowledge, right?
Speaker 2 (25:40):
yeah, and it's funny because you were talking I don't
know why this just like kind ofsprung into my head, but you
were talking about, like,understanding business.
There's also another componentof like what you did in the
current role when you were goingand getting those other
projects.
There's organizationalknowledge too, so you know the
people.
You've built some type ofrelationship.
(26:01):
There's some trust, trustestablished, so so what,
essentially what you've done inin the role that you're, you've
you're in now is you've built alot of trust.
Have you ever kind of thoughtabout that?
There's value in that right.
Speaker 1 (26:15):
Yeah, and I'm realizing that, I think very
recently, I've realized that, asI've been working through
different teams within myorganization, as I've been
working through different teamswithin my organization, I think,
(26:37):
working with different projectmanagers, different heads of
teams, I've gotten to know mywork and have gotten to know
just, I guess, who I am as acolleague, that I am very
reliable and that I do delivergood work am very reliable and
that I do deliver um good work,and so I think word of that gets
around, um, and I think thatjust being somebody who is
willing to take on differenttypes of projects that are maybe
(26:58):
outside of my like, veryspecified role, um like puts me
in that situation yeah, and alsotoo is the term that's coming
to my head is like strangerdanger, like you're a known
entity to them, so like there's,there's a track record of like
okay, well, christina's donegood work, so it's accurate,
it's done on time, but she'salso pretty flexible.
Speaker 2 (27:20):
She's like open to um , you know, stretching a little
bit.
She's also a hard worker, youknow.
Like like that personalrelationship is is really
valuable and I feel like a lotof people, especially nowadays,
with like indeed, and linkedin,and I mean there's almost this
like tinder application of thejob market, like I applied to
(27:43):
196 jobs in two weeks when I waslooking back eight months ago.
Speaker 1 (27:46):
Oh, wow, okay.
Speaker 2 (27:47):
Because, like, I can go on and it takes five seconds
to do a one-click LinkedIn apply.
I mean, it's essentially likeswiping left or right on a
dating profile yeah, but there'sso much noise that happens
within that.
It's funny because Tinder cameout after I was an undergrad but
(28:09):
it seems like people are sofrustrated with, like you said,
you've got a friend that hatesthe apps.
Now that's single and it's justa mess.
That same kind of effect ishappening on the job market.
So, yeah, I just thought thatwas kind of cool that most
people don't really think aboutthat, like the equity that
(28:31):
you're building within theserelationships.
Speaker 1 (28:34):
Yeah, I think before I did also consider like that
whole like job hopping aspect ofthings, yeah, but also I think
when that was becoming trendy itwas a very different job market
, like right now people can'treally afford to go into, to
like just quit their job and gointo another one, because, like
you realistically never know ifthat role is going to be like
(28:58):
long-term, like they couldliterally cut you whenever if
they don't think you're a goodfit.
Like if you go into a new roleand they're like yeah, actually
they're really not a good fit,like they could just fire you if
they wanted to, versus like ifyou're, I mean obviously in my
(29:20):
role.
Speaker 2 (29:20):
They could fire me whenever they wanted to as well,
but I think there's obviouslymore of a risk in job hopping.
Um, yeah, but to push back onthat, like them, they could fire
you whenever they want.
But if you think about it,you've worked at the company
what?
for two years now a year and ahalf that they would be losing
all of the knowledge that you'vegained specifically within
their business and how it plugsinto the broader market.
You know what are the problemsthat they're facing and how you
(29:40):
can proactively solve them.
That like intuition that you'vedeveloped, Like that's valuable
to them and I think thatthey're aware of that.
So yeah, there's, but on theflip side of that, there's some
people who stay in jobs too longand it's bad.
So I feel like I've beenhearing a lot of one side of the
(30:03):
argument, not really kind oflike struggling with the nuance
of both.
Speaker 1 (30:08):
Yeah, I think definitely there's nuances to
both, cause I mean, you can stayin a role for a while, like,
obviously, if I'm in my role andthen, um, I don't get like the
pay increases that I feel like Ishould be getting, then yeah,
maybe that's a sign that they'renot valuing you as an employee
and that you shouldn't goelsewhere, right, um.
(30:28):
But I think if you can continueto grow and, like you know, if
you have growth opportunities,grow and, like you know, if you
have growth opportunities withinthe company, I think that's
different because obviously, ifyou're just like stagnant and
you're not learning anything newand you get kind of bored, then
yeah, maybe you should maybejob hop if you want.
Speaker 2 (30:48):
but yeah, and it's also so like individualized
right, like learn.
It sounds like one of your bigvalues is that you want to grow
and you want to learn.
But what if Susie Q over herejust had a child, right?
And has a young family Likethat's its own set of challenges
and learning, so beingchallenged there and then being
(31:08):
challenged on the job, so it'sno like one-size-fits-all
solution.
Speaker 1 (31:12):
Yeah, it's kind of like whatever you want, like
some people don't want to growin their role or grow and
they're like well, they valuestability right, they just want
to do what?
They've been doing and they'rethey're cool with it, which is
like that's cool right like.
I guess I'm someone who can getbored easily like I like to be
challenged right also I'm young,so I don't have, like all these
(31:35):
other major responsibilitiesoutside of, like my nine to five
.
Yeah, I don't have anyone tofeed.
Speaker 2 (31:43):
You got to feed yourself.
Speaker 1 (31:44):
Well yeah, besides myself obviously All right.
Speaker 2 (31:47):
So do you have any recommendations for, let's say,
someone's listening to thepodcast, that's like a sophomore
or junior in college right now.
Do you have any like advice orreflections that you would want
to pass on to them?
Speaker 1 (32:02):
I would say, to explore like courses that, um,
maybe are not, if you have theability to explore courses that
are outside of your major, justto see like what's out there, to
really try to get an idea of,like what you want to do after
(32:24):
college.
Because, like I thought, Ireally thought I wanted to be an
actuary and now, looking backat it, I don't think that's
something I could have done.
Speaker 2 (32:34):
Really Mm-hmm.
That is kind of cool becausewhen you were in my class you
kind of got your first exposureto real data, right, and it
sounds like I mean, obviouslyyou enjoyed it to some degree,
right.
Speaker 1 (32:49):
Yeah, I did.
Speaker 2 (32:53):
Very cool.
What about it?
Did you was the?
Because I remember when I firstgot exposure to like real world
data, it stressed me out.
I was like this this is notformatted correctly, it's not
accurate, there's missing data.
How to clean that up?
But it sounds like with withyour experience of, it was like
(33:13):
oh, this is a challenge yeah andthis is new, and this is like I
don't know.
Speaker 1 (33:19):
You're solving a problem that's not been
specifically solved before yeah,I think it's just like being
able to, because I think there'sa lot of like creativity when
it comes to like looking at data, um, that a lot of people don't
necessarily think because it'slike oh, it's just like
analytics, like very liketechnical, but I think there's a
lot of creativity that goesinto like looking at the data
(33:41):
and trying to, like you know,gain insights from it, because
you do have to look at it fromlike all sorts of angles.
And even just the data cleaningprocess itself, which is kind of
like the lengthiest processmost of the time.
I think you could learn a lotabout the data cleaning process
itself, which is kind of likethe lengthiest process most of
the time.
I think you could learn a lotabout the data with just like
cleaning it, um, and I don'tknow, I think it's just that
iterative process of like okay,here's the data, let's clean it,
(34:04):
and then let's like visualizeit and see what we can gain from
it.
Like here are the questionsthat we have as a business.
Just that there's like a lot ofum, there's a lot of freedom to
to solving like problems whenit comes to data yeah, that's
true and the position I'm in now.
Speaker 2 (34:24):
There's multiple ways that you could solve the
problem, but that is actuallysecondary.
What the main thing is.
It's almost like being a lawyerin court You've got to be able
to justify what you're puttingout there.
Because, they're not alwaysgoing to say, hey, this seems
off, but they could at any time.
So you've kind of got to beready to justify whatever
(34:46):
recommendation you're making.
Speaker 1 (34:47):
Yeah, and I think you have to be ready also to look
back at your work and be like oh, maybe I did make a mistake,
Maybe I did look at somethingincorrectly or maybe I'm not
looking at it through the properlens.
Speaker 2 (35:00):
The right lens yeah.
Speaker 1 (35:01):
And that's happened to me a lot at work, and
sometimes there's people in theorganization who have experience
and knowledge on a certainaspect or a certain channel,
like in marketing, like directmail or print.
I don't really know much aboutthose things.
Speaker 2 (35:20):
Those are like old school marketing, right.
Speaker 1 (35:22):
Yeah, exactly, that's like the old school marketing.
And so somebody might have alot of knowledge on that that I
just simply don't, because Ijust don't know much about it.
And so it's like if I'mpresenting results and they're
looking at it like oh, that thatdoesn't seem right or like are
you, you know, can you tell memore about this result?
And if I can't really say muchabout it, then it's like okay,
(35:43):
maybe I have to go back andreally question what the output
is from this data set.
And right, you know, maybebring somebody else in to be
like hey, hey, what do you thinkabout this?
Speaker 2 (35:55):
So I've got a question for you, because I'm
one of these Are you more of abig picture thinker or more of a
detail person?
Have you been asked this before?
Speaker 1 (36:08):
I don't think I have.
Speaker 2 (36:09):
Oh, that's a good question.
Speaker 1 (36:10):
I'm sure I feel like I'm.
I'm probably more, I would say,like the small.
Speaker 2 (36:20):
I was gonna say my, my understanding of you, or like
at least what I've seen.
You seem very detail, detailoriented yeah, to where.
I'm much more of a bigger like.
I'm much more interested in thestrategy aspect of it okay and
like less the kind of innerworkings of like the algorithm
I'm like is this right or themodel?
Speaker 1 (36:40):
that's being built Right right.
Like you, like kind of buildingthe model right yeah, and I get
into the nitty-gritty of likewhat the model is doing.
Speaker 2 (36:47):
I would like for someone to kind of build that
out and then I can use it tothen drive a business decision
and then I like quantifying that.
That's kind of like where Ilike to sit on things, okay,
which kind of makes sense.
Now, as to why you're goingmuch deeper into the technical
(37:07):
stuff, because, like you, like Imean, I wonder, it seems like
we're just kind of wireddifferently, huh.
Maybe, Like well, because, likedo you remember why you thought
it was a good idea to get a mathdegree?
Because from my perspective,like that seems like pulling
teeth.
Speaker 1 (37:26):
Because I really like math.
Speaker 2 (37:27):
Really.
Speaker 1 (37:28):
Yeah, that was just my thing.
I was like okay, I really likemath and I'm good at it, so let
me just my thing.
I was like okay, I really likemath and I'm good at it, so let
me just do that.
Speaker 2 (37:36):
See, I can remember in high school having a moment
where I was like I might havebeen trigonometry or something.
I was like I don't like this.
I don't know if I can gofurther with this.
Speaker 1 (37:47):
Yeah.
Speaker 2 (37:48):
Like I don't know, math felt overwhelming to me,
especially when I was younger,but it sounds like your exposure
to it.
Speaker 1 (37:54):
Yeah, I've always liked it.
Speaker 2 (37:55):
It was like more of like a curiosity-inducing for
you.
Speaker 1 (37:59):
Yeah, I thought it was I don't know, I just thought
it was cool, like I just reallyliked math always.
Speaker 2 (38:05):
Okay.
Speaker 1 (38:06):
Just like really nerdy.
Speaker 2 (38:07):
What's cool about like just the fact that it's
logic or fact-based?
Speaker 1 (38:14):
Yeah, and I think it's just like.
I mean, I really like the waythat it can explain or that it I
guess it like parallels, likenature in so many ways, like
that's one of the mostinteresting things about math to
me, like the.
Fibonacci sequences, like youknow, like the number 108 of the
universe, just things like thatalways fascinated me and I
(38:37):
think obviously when you'reyounger you can't understand the
actual mathematical concepts ofthem.
When you get into college youcan begin to really understand
these things.
I don't know, I just thought itwas really interesting and a lot
of things kind of just clickedfor me.
You know, begin to reallyunderstand these things, and I
don't know, I just thought itwas really interesting and it
(38:57):
just a lot of things kind ofjust clicked for me that maybe
not everyone else like just getsit as easily, just because they
probably don't have an interestin it.
I think it takes an interestfor it to be easier to learn.
Speaker 2 (39:09):
Yeah, Well, I mean, I think it's just interesting
that, like we're both in kind ofthe analytics, you know, space,
but we're very, two different,two very different personality
types.
Because I'm much moreinterested in like big picture,
you know.
I mean I used to joke like whenI was running silverton
analytics, like a lot of thedata visualization work, I would
(39:31):
be like this is bigger thanthis, so you should make this
decision.
It's like basically makingpicture books for influential
adults, like if I was being alittle cutesy about like saying
what I, what?
Speaker 1 (39:42):
I do yeah.
Speaker 2 (39:43):
But you're like no, I want to like, build out the
algorithm and get into.
Ai and all this crazy stuff.
Speaker 1 (39:49):
Yeah.
Speaker 2 (39:50):
But I mean, I think just being aware of that is is
valuable in that you can startto inform like, like what's next
, like what, what?
I mean?
Are you thinking about gettingto like more into, like data
science?
Speaker 1 (40:04):
um, I think my next step, uh, that I've been
considering this year, I wantedto get a master's, okay, yeah,
so I'm considering a master's inai oh wow, that's really cool I
might do that.
Speaker 2 (40:18):
I might apply in june so tell me more about is what
man.
I'm gonna sound like a boomerhere.
Well, what is chat?
Gpt?
Is that ai?
Yes okay, yeah, but and what isit doing exactly?
I mean, I sound so dumb rightnow.
Speaker 1 (40:34):
Yeah, I mean I guess it's just.
I mean, at the end of the day,it is kind of just like a
chatbot, because it's beingtrained on what input you're
giving it, but also like,obviously, when they built it it
was trained on like all thisdata that we have.
Speaker 2 (40:51):
So is it like a?
It's just like machine learning.
It's like a series of nestedifs and statements.
Is that what you're saying?
Speaker 1 (40:57):
Sure.
Speaker 2 (40:57):
Isn't that what the joke is right?
Speaker 1 (40:58):
now yeah, no, I mean it goes a lot deeper than that.
But yeah, I mean, it's just.
Speaker 2 (41:06):
Very cool.
Speaker 1 (41:07):
Kind of like an advanced chatbot.
Speaker 2 (41:14):
Have you been on any of the episodes with michael
galarnik?
I think, so so he's studyingmachine learning for finance,
okay, at georgia tech.
So he I need to connect youwith him because he would be
somebody.
So are you thinking aboutgetting a master's or he's he's
doing?
a master's, I don't think Icould do the yeah, well, because
like that is, but getting a phdin a, in like a practical space
, like that, I think it's areally interesting concept.
(41:36):
Yeah, because you could teach,but then you could also go work
for you know some type oftrading company and build them
an algorithm that's worthmillions upon millions of
dollars.
Speaker 1 (41:47):
I guess yeah, it's always an option but alright,
cool.
Speaker 2 (41:52):
Well, christina, thank you for for joining us.
This has been really fun.
Speaker 1 (41:56):
Thanks for having me, it's been great.
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