#255: Dear APH-y: Career Inflection Points

To data analyst, or to data science? To individually contribute, or to manage the individual contributions of others? To mid-career pivot into analytics, or to… oh, hell yes! That last one isn’t really a choice, is it? At least, not for listeners who are drawn to this podcast. And this episode is a show that can be directly attributed to listeners. As we gathered feedback in our recent listener survey, we asked for topic suggestions, and a neat little set of those suggestions were all centered around career development. And thus, a show was born! All five co-hosts—Julie, Michael, Moe, Tim, and Val—hopped on the mic to collaborate on some answers in this episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Episode Transcript

0:00:05.8 (00:02):
Welcome to The Analytics Power Hour. Analytics topics covered,
conversationally and sometimes with explicit language. Hi everybody, welcome
to The Analytics Power Hour. This is episode 255.
There's a quote that says, "If opportunity doesn't knock, build a door."

(00:24):
Analytics careers have lots of opportunities. And thinking about your path
and being intentional about curating your career journey is really important.
And as you go through your career, there are inflection points along the
way. We all face them. Sometimes they require big risks and other times
they just require saying yes or no to the right thing.

(00:44):
We did a listener survey and we got a series of questions from
our listeners on this topic. And so I think it makes a ton
of sense for us to jump in and explore this topic in more
detail. And as always, I just wanna give a huge thank you to
our listeners that sent these questions in as part of the survey responses.
So my name is Michael Helbling, and now I wanna introduce you to

(01:05):
my co hosts. Moe Kiss, welcome. Hello. Nice to be here.
Pretty exciting. We're recording this show remotely, but on the same continent.
So that's pretty awesome. It's a first. Julie Hoyer, welcome. Hello. That's
awesome. Val Kroll, welcome. Hey. Hey, pretty people. And Tim Wilson.

(01:29):
Welcome. Oh, there was a welcome. There was pause there. I thought about
it and then I was like, I better just
be nice 'cause it'll probably get bad later. That's right. It'll be the
off air. We know what it's like off mic. Yeah. No,
but I think... So this is awesome 'cause I feel like this is
such a great group of people to answer some of these questions because

(01:50):
we have such a range of different experiences in our careers.
And so let's just dive into it. Let me pull up the first
question from our listener survey. So this is somebody who said,
"I'm a software developer with strong SQL, but I have very little stats
background and I'm about to be named an analytics lead for a large
team. What should I learn first?" I actually do have experience of

(02:20):
having a manager who was an engineer. So like backend engineer.
And there was definitely a steep learning curve
that we had to go through. I think it also kind of depends
when you say analytics lead, are they like technical lead or are they
manager lead? Because the answer is very different. So it sounds like probably

(02:44):
they're gonna be managing a fairly large team as they step into that
new role. Yeah. Let's go with they have both. Maybe it's even organized
that way. No. You're saying it wouldn't happen? They'd report into different
areas? At first, learn a better org structure. No, I'm just like,
it's gonna make the answer about 40 minutes long. Well, we better get

(03:06):
started, Moe. I would say like the strong SQL, like there's got to
be some level of figuring out how to be a...
You probably have a strong bias towards solving the engineering problem.
How do I get the data? Where do I get it?
What are the caveats? And there's gonna be a big
need to sort of resist that impulse. I assume you probably have other

(03:31):
people who will have that impulse in spades and the need to
build relationships and ignoring the data and just figuring out what problems
the business is expecting you to solve and whether they're solvable or not.
That would be my... That's like, what do you need

(03:52):
to learn? It's like, well, probably it's unlearn. I mean, those skills are
needed. I'm not saying you need it. It's more like, how should you
spend your time? And I think the thing is when someone from engineering
comes into the data world, they have a pretty good
model of how data flows from one place to another. They get the
kind of architecture pretty easily, I think, because they've normally worked

(04:15):
on problems that solve that. But the bit that's actually really complicated
is really understanding that niche role that data plays in being a business
partner, answering really complicated questions, sometimes with high levels
of uncertainty and still being able to progress things forward for the team.

It's all soft skills. Yeah. 0:04:35.9 (04:33):
Yeah. And I would say the question
specifically said strong SQL, but very little stats background. And so
one thing to just call out is to keep in mind that you
don't need to know how everyone does their job on your team to
be a good manager or leader for those people. I think it's a
lot of what Moe and Tim were just saying about connecting to the

(04:55):
right business problems and understanding what the business expectations
are, soft skills, building relationship with your team. I think don't have
this urge to like, "Oh, I need to learn all the details."Because that's
gonna first of all, lead you down a path of micromanagement and also
keeping your eye not on the ball of the new responsibilities you're taking
on in this new lead position and role. Yeah. And I'm sure this

(05:16):
is part of the normal stepping into a role like this,
but if you were to actually go and talk to everyone on your
team, especially people maybe on the side where they're using the data after
you've worked with it, set it up, whatever, you may be surprised to
find out what they feel like they need in their role.
Or you could even just be asking them, what is your day to
day? If you don't know, I think that would help you start to

(05:39):
get into that phase two of figuring out how do you best help
unlock their day to day stuff or what are their big pain points?
Or do they feel like they don't know enough about the technical side
and maybe you're able to step in and just help even the team
communicate better? Because to Val's point, you don't need to necessarily
know all the stats, but if the people on your team are able
to better collaborate, that might be a good starting point.

(06:02):
Actually, Julie, that's a really good point of you need to figure out
who knows what on your team so that you know when you have
a question on experimentation or a task, who is the best person who
knows the most about that and who's potentially gonna be able to upskill
junior people that know less? I totally plus one Val's comment.
You do not need to know how to do everyone in your team's

(06:23):
job, but you need to know who to ask when that problem comes
up. And so it could also be not what do you need to
learn from a technical perspective, but what do you need to learn about
your team? But I'll kinda take a slightly different, because Val, as you
pointed out, they said the very little stats background. And I wouldn't
want to over index towards I have people who know stats,
I don't need to learn stats. And this will be probably the soapbox

(06:46):
being mounted, but the SQL piece is very deterministic. Did I query the
data? Did I get it? I think there's the understanding of statistics,
not necessarily being able to do a linear regression or an ANOVA or whatever
it is, but I think there's something to get comfortable with
what the data can't tell you, the world of

(07:10):
uncertainty and maybe a light introduction to causal inference. If you find
people on your team who have statistics or a data science background,
they may be a good resource to leverage, but 'cause my concern is
that you would go, the danger moving in that direction is that you
talk to the business and then you start steering yourself towards problems

(07:32):
that can be deterministically answered, which there may need to be some
level of education. Here's the business problem. What can we do
to run an experiment? If you're doing SQL, you're probably not thinking
experiments, even though experiments may be the best way to address some
problems. I do think you would learn enough about that in the course

(07:53):
of your role. I just wouldn't say that that's the top priority or
even in the top five. I'm like, you will learn
about experimentation through the course of your job. That's inevitable
because you will have conversations with the team about what's possible
or not possible on a task. Sure. I would just say that it's
just don't underplay how important of a responsibility it is to be able

(08:15):
to advocate for your team when you're in a room that they're not.
Because now you're a lead, so you're gonna be pulled into conversations
that you might not have been before and someone's gonna turn to you
and say, "Well, what can we do with this?" And so you're gonna
be able to speak to it where you're not writing checks that your
team is pissed that they have to cash or they're gonna be like,
"Oh, I could have done something really cool, but now I have to
walk into this conversation immediately disappointing those business partners

(08:38):
because I'm not able to pull this rabbit out of a hat."
So I would say that I agree with you, Moe, that there's a
lot of things that we can prioritize before taking a Coursera course,
that there is a lot that's gonna be on the job.
But don't underestimate how important it is to be able to speak to
the capabilities and the way that your team can be most valuably serving

the organization. 0:09:02.7 (08:59):
It's time to step away from the show for a
quick word about Piwik PRO. Tim, tell us about it. Well,
Piwik Pro has really exploded in popularity and keeps adding new functionality.

0:09:14.1 (09:13):
They sure have. They've got an easy to use interface,
a full set of features with capabilities like custom reports, enhanced e
commerce tracking, and a customer data platform. We love running Piwik Pro's
free plan on the podcast website, but they also have a paid plan

that adds scale and some additional features. 0:09:35.1 (09:29):
Yeah, head over
to piwik.pro and check them out for yourself. You can get started with
their free plan. That's piwik.pro. And now let's get back to the show.
All right, great coverage of that question. Let's jump over to a new
one. So this person asks, "I'm currently out of the workforce.

(09:53):
What can I do to maintain or grow my skills?" Coursera. I mean, I say this,
if you're out of the workforce, like it seemed like it would start
with figuring out what you wanna do when you re enter the workforce.
I've talked to some people now who are out of the workforce and
there's a tendency to, if they're looking to get back in the workforce,

(10:13):
they're like, "I gotta add skills to my resume." And they're trying to
find the things that they can get certified on
so that they can bolster their resume, which easy for me to say
that feels a little dangerous because it's not grounded in
what was it that as I re enter that I most
want to be doing. Yeah, Tim, maybe you spent a long time out

(10:37):
of the workforce before you started Facts and Feelings, so.
Didn't learn shit. I think the thing as well is don't underestimate your
network. Totally. 100% I actually was chatting to someone
recently who through no fault of their own has been out of work
for a little while and they decided to take a couple months off,

(10:57):
relax, that sort of thing. Then was like, then I'll find another job.
And the reality was that the market is not what it used to
be. It is actually really hard. And they're finding it quite a struggle.
And the biggest reflection he had was in that period of time off,
I didn't put time into my network. And that's actually
so valuable. So like, I don't know, I get a lot of resumes

(11:19):
with people who have just done 50,000 Coursera courses and things that that
makes them super qualified to do the job.
And like, sure, it can add skills, not disputing that.
But the reality is, if I was out of the industry for a
bit, I would probably still keep going to meetups. I would still keep
having coffees with people, which is actually what I did when I was

(11:40):
out of work, like keep my network alive. And I would also keep
listening to podcasts 'cause I feel like meetups and podcasts are a very
good way of staying across changes to then know what things you need
to learn to fill that skills gap. So I don't know,
like maybe I would have a very different perspective on what to do. But
this wasn't I mean... I wasn't reading this question as saying,

(12:01):
what should I do to find a job to re enter?
It was they're specifically saying they wanna... Yeah, keep their skills
up. I mean, I think I don't disagree. I also think it's one
of those things that it's like, don't wait until you're out of a
job to decide to start networking. Yeah. But in that vein,
I do think there's many non for profits and even like local mom

(12:24):
and pop businesses that have zero analytics capabilities, zero access to
those skills and would be great places to contribute. And especially if
it's things that you already care about or you're interested in,
it's a great connection to make. I know
that is a place where I've spent some time doing some small things

(12:47):
for different groups and it's both personally rewarding as well as you get
to answer some questions and work through some problems with teams and sort
of burnish those skills. And honestly, I think, if you are planning to
re enter the workforce or get back in the workforce, it looks good
on your resume that you're doing things like that and contributing like
that. It's great for a behavioral interviewing question when you're like,

(13:10):
"Oh yeah, by the way, I helped feed 10,000 kids by doing a
little database work." Or something. It does depend though on the reason
they're out of work. Sure. And the reason that I say that is
I can imagine a person asking this question if they're on parental leave.
And I think sometimes when you're on parental leave,

(13:31):
you have this like expectation of yourself that you're going to be able
to do all this learning and volunteer. Like I volunteered to do a
project on some equitability data on my first parental leave. Do you think
I did any of that? No, not a single thing.
As I recall, you went from, you were missing it, so you're like,
"I'm trying to let them do it." And then they're like,
"Sure, have at it." And you're like, "Well, what the fuck was I

(13:51):
thinking? I don't have the energy." Yes, I was like, no, I do
not have the... I don't have the time. It wasn't so much the
time. It was more the energy and the mental capacity, right?
I mean, it's like, right? Yeah. Right? Yeah. But I do think you
need to be careful. I actually did do a lot of data work
when I was on parental leave about timestamps since the last time my
kid pooped, ate, slept. Yeah. That data collection.

(14:14):
Data collection, exactly. That's actually not a bad idea. Personal data,
like... There you go. Doing things with your own data is kind of
interesting. I mean, I didn't do anything with it. I just collected it because
again, I overestimated that I would have time to do that.
And then. You collected it just in case, just in case you needed

(14:35):
it. It's up and you're like, "I feel like work." Ooh, Neck Gershoff burn.
Yeah. That's right. You know what? I have a really nice one year
summary though, that I was like, "Wow, I feel really accomplished."
All the, all the things I did collect just for that little pat
on the back. I was like, "Worth it." Nice. The decision making at
the margins that did not happen. Yeah. You know what? Or when you're
just sleep deprived and you're like, when's the last time they did X,

(14:57):
Y, or Z? And you're like, "Oh, let me go to my app."
Look. By the time you have your fourth kid, you're gonna be like, "
I might wanna do a comparison across all of the children."
It could happen. So. Yeah, probably not. Four we're kids, who has time
for that? And we don't mean to assume that the asker of this
question was someone who's on parental leave or not. It was a good example.

(15:17):
But it was a good example. For Moe to stretch us to think it's not
just about some of the... Yeah. Yeah. Okay. I like it.
All right. Next up. "I'm mid career and pivoting into analytics.
What skills do I need to focus on most?" R. Yeah. No,
I think SQL's number one. I mean, a lot of this depends on

(15:41):
where you're coming from. Yeah. Because there's some real complimentary
career paths that flow into analytics really nicely. So like people from
finance or things like that. And so depending on where you're coming from,
might do that. I think SQL's a great answer,
less so R, but I get where you're coming from, Tim. But I

(16:04):
think the thing that I've observed about people who make those transitions
is how can you leverage where you're coming from into where you're going.
And so in other words, you've learned a lot of
very valuable skills and actually creating a lateral bridge of leveraging
those skills into your analytics career is actually a huge resource for

(16:27):
you that you might not realize. And so that's what I'd first encourage
you to do is just think a lot about and meet with people
who are in the analytics field you're looking to get into
and collaborate on thinking like what skills have I learned that served
me well? And then use that to build out a skill map of
like, "Okay, now here's where I've got things I need to learn versus

(16:47):
where things I've learned now I can translate those over." And I think
that'll really help you realize that maybe you're not as far off from
this career switch as you thought. I would say two communication skills
as an analyst. I run into a lot where people aren't even thinking
of how are you going to make sure you document the process of
pulling the data to answer the question? Or did you even document the

(17:10):
question being asked? It's like some of that more hidden
part of the process. But I do feel like as an analyst,
like you have to own that you have things like you have a
clear process, you have documentation, you can go back to it,
you can communicate what you did, you can communicate the assumptions you
made, or based on the question being asked, did you take a pivot

(17:31):
on the question halfway through and you didn't realize it? I think there's
a lot of those quote unquote, softer skills or more hidden skills.
But because when you get to the point of having to communicate the
work you did, and present back to people, I feel like I spend
a lot of time coaching people in that area. And I don't know,
depending on like you said, Michael, depending on where you're coming from,
if that wasn't a normal part of your role, it does take a

(17:54):
lot to be able to make a deck a short one to give
a readout from how much are you going to share? What kind of
data visualizations are you going to use? There are some basic
tools you need, I feel like to be an analyst that can function
well with your stakeholders. They're the skills that you're not really gonna
understand why they're so critical until they bite you in the ass a
couple of times, like the documenting your process, documenting your assumptions,

(18:18):
writing out a plan, that sort of thing. And then I think there
are skills that are relatively straightforward that I think are critical
'cause Julie, as you were saying, communicating, I mean, I know I have
a little axe to grind about data visualization, putting that even separate
from data storytelling. You can read one book, you can read Cole Knaflick

(18:38):
or Alberto Cairo or Stephen Few, read one book and it will
change the way you're thinking. Now, Michael, to your point, if they're
already somewhere where they're like, "No, I'm already doing this stuff
all the time and I already know these principles." If you don't know
some principles of data visualization, then I would think that because it's

(18:59):
kind of quick, it gets you to a base level very,
very quickly is one of those things. The other part is gonna be
very situational, where you're if you're pivoting into analytics and you
have a role, then you need to also include in your sort of skill
map of what's most needed here. I need to
talk to my peers and talk to others and figure out which are

(19:21):
the biggest gaps that I can fill with some education, which are things
that are gonna take a little longer, but I should be riding along
to do them. If it's. I'm just gonna pivot generally into analytics and
it's very, very broadly defined. That's Really, really
hard. Like, I look at the analytics subReddit where they're, sometimes people

(19:43):
are like, what the hell? Just I got this job, I thought I
was gonna be doing X, instead I'm doing Y. Is this normal?
And it's like, Nobody, it's not normal. Like nothing's normal. Everybody
hits that in some way. Like if you're expecting some
tight little list of, this is the definition of what this role is.
It's not. You can't learn it all. You can't learn it all now.

(20:05):
So you're gonna have to find a way to focus. Yeah,
definitely. Read a data visualization book and then realize every reporting
tool you interact with isn't gonna let you do the thing you want.
And then learn R to Tim's point, right? Yeah. 'cause then you can't.
Maybe to focus on the business you're going into because maybe you have
a lot of context moving into analytics, but if you don't,

(20:27):
like you're making a jump and it's a new business. I would say
focus on learning the business process, what they care about. Because that
context will help you decide what you actually have to learn about the
analytics like execution part. But you can stay grounded more in what's
gonna matter instead of getting sidetracked. Yeah. And I think that's the
only other thing I had in my head besides some of the awesome

(20:47):
tips you guys had to share, is to think about some of the
nuances of where you practice analytics and understand what you might gravitate
more naturally towards. Like based on some of your strengths or the things
that you've enjoyed in past roles. 'cause it can look very different depending
on where you're at. If it's consulting or in house, just as you know, lots
of different flavors. And last bonus tip, learn what a McKinsey headline

(21:08):
is or an Insights headline. Like whether you're using it in an email
as like paragraph titles, whether you're using it in a presentation,
whether you're using it as like the headline
for like a writeup you're doing. Learn what they are. Like your life
will be phenomenally easier and people will understand what you're saying
or what the team are saying. It is hands down, one of the

(21:31):
most important skills I think, and it's like a little bonus one that
most people don't think about. It's bonkers if you search for,
and not doing it right now. The last time was a couple years
ago where I'm like, surely if I search for McKenzie title, there'll be
like a million posts and like there aren't, but it's like this is
so simple. So if you search and you're not finding it,
it's literally... Really? Yeah. It's crazy. It's like buried in little niche

(21:54):
places where... Maybe I should write a blog on it. Another thing on
my to do list I won't do. Yeah I mean, If somebody doesn't
know what it is and they're like, "well tell me what it is."
Just, it's making it a declarative statement. Like make your title.
Don't put results from last quarter. Make it a declarative statement at
the beginning of a paragraph at the top of a
slide. Once you start doing it, you can't unsee how ineffective it is

(22:18):
when people don't do it. And you feel it right away In terms
of impact. I'm sitting here thinking, can Generative AI make these for me?
I don't know, probably not very good ones but... And if you struggle
to make one, then maybe you don't have a point. That's Actually interesting
too, Michael, on the AI, because if you just had a slide with

(22:38):
the data visualization, it kind of proves the point of like,
what would anyone else take away from the data? Like you're supposed to
use the McKinsey Title to tell them what to focus on.
And like the slide is there for them to validate your title.
So it would be funny to say, "this is what I would say."
and then show it to AI and be like, what the heck would
they say from this slide? 'cause it might be really different.
Oh, it's like a quality Check on here. Although there could be a

(22:59):
feedback loop where you keep refining your visualization and the way that
you present it until the AI actually says it. Then you've got reinforcement.
Torture it? Yeah It's been a while since we've said McKinsey Titles on
here. That's all. That's a good one. Love that. I think we gave
more than they bargained for on that one. So it's good. Yeah. What skills...

(23:22):
Just learn the one. Just learn this one thing. Yeah. That's right.
Oh yeah. Probably pivot tables. Learn pivot tables. Alright, next up.
"I've been a data analyst, but now I'm trying to move into data
science. How can I best do that?" My advice don't. No, I'm just kidding.

(23:46):
I wish I could follow up and ask them what is drawing them
to data science. How are they describing these two roles?
The pay differential? Yeah. I mean that was absolutely
me seven or eight years ago. 'cause I thought the industry was going
that way. Now I feel like data science is almost, it's gotten so

(24:12):
liberally spread out to anyone in any kind of role that is mildly
technical. I don't know. Moe, do you feel like the data science
role at Canva is reasonably tightly defined? No. No. Okay? No. And it
actually is a huge problem 'cause we actually were all called data analysts

(24:33):
and we also had business intelligence analysts and we made the decision
to call everyone data scientists. And one of the things that happens through
like the performance review cycle is, there is an up weighting of people
say that build tooling or work on experimentation or create models,
that sort of thing. And one of the things that has actually come

(24:54):
up that's kind of a conflict is we have some very incredible people
who answer the most complicated business questions out there. But it is
what you would describe as like a typical data analyst role.
And sometimes like it doesn't fit. 'cause our definition of data science
is not actually representative of the skills we have. And so it's a

(25:16):
point of tension that we're trying to work through at the moment of
like, how do we accurately reflect what very good performance is when your
skills are more on like the business acumen or commercial side than they
are on the like hardcore data science side? I mean certainly from an
analyst perspective, shifting it toward data science. Obviously, you need

(25:37):
to pick up and learn a programming language of a couple of kinds. Probably
Python maybe, R maybe both. 'cause those are gonna be critical.
But I think, I mean to your point Moe, like there's not seem
to be like the only thing I see a lot of times in
organizations where there's a sort of distinction between, here's our analytics

(25:57):
team and here's our data science team is the data science team is
typically working on broader longevity types of business questions.
Whereas the analytics team is working on more short term, more like observational
and/or day to day business questions. And I don't know if I agree.

(26:19):
Like I don't personally think that's a great way to organize around data
and analytics necessarily. So in a certain sense, like I struggle with the
fundamental underpinning of where that lives. Because as analysts, we should
be looking to tackle all along that and building our skill sets up
to go into things like complex experimentation and segmentation and cohorts

(26:41):
and those kinds of things. And you build your skills up.
And at some point, you tip over into becoming a data scientist instead
of an analyst. I don't know, somebody probably has an answer to that
question, but I feel like the frontier is pretty vague.
I will agree with Tim that like five,
seven years ago that it did feel like the way to like future

(27:02):
proof ourselves to make sure that we stayed relevant. Stayed on top of
our skills. But I will say that what I personally have noticed a
lot more of as a way to future proof yourself is to broaden your
definition of the analytics that you work with or the data sets that
you'll touch. And so you are able to answer more complex business questions
if you're more rooted in the data that runs your business.

(27:24):
And so I also agree with Julie that I wish we understood a
little bit of that inspiration. 'cause you know, hey fair point,
like if you really want that pay bump, pay increase, like different things
motivate different people. But it does seem like especially if you're in
house, that there would be a way to like weasel your way into
certain projects that have some of these skills required or some of the
projects that data science team is working on. And I think there was

(27:47):
like a couple episodes that we've done in the past that we can
link in the show notes that have some great examples of ways that
you can start to build the skills or to gain those experiences that
really excite you. Because again, if you're drawn to or pulling yourself
in the direction of the things that you're most interested in about the
data science world, that's what's gonna make it the easiest, most fun,
the most rewarding, the most engaging. Well that is reminding me and Robert

(28:12):
Petković listens to this. He will reset his time around, reminded me of
this phase in my life for another four years. But I went for
like, "oh I'm gonna become a data scientist." Pretty quickly said,
"well maybe I was gonna be more data science y was kind of
my shorthand." 'Cause I was figuring out in a semi public way,
like what does this mean? And I think ultimately where I landed was

(28:33):
I'm expanding my perspective and skills as an analyst. Like absolutely thrilled
that I learned R. If I went back to previous me,
maybe I would've tackled Python instead, can I, without even looking at
documentation, run even a simple linear regression in R? No. So I use
it for all sorts of stuff that's useful. Same thing with Python I've

(28:55):
gotten to where I can kind of scan it and read it.
I would consider myself still very much an analyst. The things I've picked
up along the way are a better understanding of some of the deeper
principles and capabilities and ways of working. But I think I gave up
pretty early on of like trying to reach, but that makes it sound

(29:15):
like it is a progression. So I don't know that you just sort
of tip over into being a data scientist. I think Cassie Kozyrkov would
say she doesn't say data scientist, so she's like "the analyst and the
statistician or the analyst in the decision scientist. And it's along the
lines, I think Michael where you were was the analyst is a little
quicker hit. They're finding things or doing explorations. They may be doing

(29:38):
a linear regress, they may be doing regression, they may be doing other
things to say, is there something here?" The statistician says, okay,
now we're gonna put the real rigor on it. It could be we're
building a model that's gonna be productionized. That's often a data science
role." It could also be, "no, we're gonna build a model.
We're gonna appropriately split the data set into a

(30:00):
training and a testing thing. You get one shot at it."
So there, there is a higher level of the question being answered.
You have fewer shots, there's more rigor, it takes a little more effort,
but you are trying to get to something
that you can really move forward with. Whereas I think she looks at it as
the analysts a lot of times are kind of inspiration

(30:21):
for those smaller things or the select number of things that then bubble
up. Yeah. It's funny, when we came to this question, the first person
I thought of was Cassie, but not because of her definition split. It
actually comes down to her like, just be useful thing.
Which definitely comes top of mind. And I guess one of the things
I think you really need to think about is your motivation.

(30:44):
I feel like there are too many people that are like,
"I wanna do data science stuff because I wanna do the cool,
sexy, fun stuff." Like I've had people literally come to me and be
like, "I don't wanna do the like 10% boring bit. I just want
my job to be all of the fun, sexy stuff." And I'm like,
"that's not realistic. Like that is not a thing." And so I think
you just need to make sure that it's not like you're chasing the

(31:06):
shiny thing. It's like if you were genuinely more interested in that work,
that's cool. Like that's not a problem. But like you really need to
interrogate your why of why you wanna make that move. Can I just say
a little tactical too, like depending on what data science and data analysts
mean in your organization, like try to partner and work with a data

(31:27):
scientist. I know I get to do that at my company and
so the way we interact, I'm able to bring some context maybe,
but I'm also able to learn from them like the rigor of the
different methodologies and tell them like, "here's the business problem.
You know, here's what I'm really trying to focus in on.
Here's maybe what I hypothesize is happening, to best answer this though,

(31:48):
Like I was thinking maybe I use this type of regression or whatever
it may be."And then I can have a conversation with them where they
are bringing in that deeper knowledge of what are the tools we could
use here. So maybe even just thinking, can you do that to help
you in your transition? Because those are the people if you're wanting to
stay in that organization, like they're gonna highlight to you where you
have the skill gaps too to focus in on. And then to Moe's point,

(32:11):
like you can decide is this really what I want my role to
look like and grow into? So the shadowing or like partnering would be
my biggest tip. Yeah. I'd also throw out there that I feel like
data science as a profession is more impacted by economic cycles
than an analyst typically is. And so you'll find

(32:32):
potentially in the job market more or less opportunities depending on sort
of where the economy's going and the cycle of the economy.
Certainly a lot of people I've talked to in the last year,
year and a half who've been looking for data science roles have definitely
noticed some shift in the uncertainty in the marketplace has caused companies
to pull back on data science hiring a little bit. And it kind of goes

(32:55):
to some of what we've been talking about. You know, when a company
is more uncertain, they take shorter swings at bigger deals and so they're
pulling their risk perspective back. And so the big analysis or segmentation
or pricing strategy that long term project they might have taken on before,
they'll now put on hold. Whereas they still need to run the business

(33:15):
day to day find opportunities and do the analysis around those things.
So you might find that as well. And so that's the other thing.
If you're not finding opportunities in the job market to transition into
data science roles, it may require some patience. It's not you,
it might just be the cycle of the economy that we're in.
So hopefully somewhat encouraging. Don't feel bad if it's sort of like,

(33:38):
man, "I've been trying to be a data scientist and no one will
hire me. It might not be you. Alright, great. Let's jump into a
new question. So this is actually one where we've actually done some topics
around this, but I think it's ever present. And so let's jump into
it. "I'm at a point where I need to choose whether to pivot
from being an individual contributor to a management role.

(34:00):
What should I do?" That's the listener question you're not asking.
Yeah. That's the listener question. Yeah. Yeah. That's... Schmichael Schmeling.
That's right. Pay no attention to who asked the question.
Just answer it. Tim, for God's sake. Sorry. Is the question,
what should I do? Should I pivot to being a manager or is

(34:22):
the question... No, I think it's whether or not. Yeah. Should I? It's at
a point where I have to choose. I mean it... I don't think
we're gonna get to a good answer dear listener about what you should
do. No, I actually think we can. Oh. You know what that means,
Michael? Let's do it. I see material Moe, is management material.

0:34:43.5 (34:42):
Oh, well that goes without saying. Absolutely agree with that.
I think It comes down to like, do you wanna be the one
doing the work or... The thing actually that is probably the biggest determiner
for me. I was interviewing someone a couple weeks back
and I said to them like, 'cause it's a person who actually is

(35:02):
an IC at the moment and we were considering them for a like what
we call a coach's role or a manager role. And I was like,
what's made you wanna go back? And he's like, "I actually just love
developing people. I love coaching a team. I love helping them achieve like
their goals, work through problems. Like yes, I'm an amazing IC but that's

(35:23):
not the thing that excites me the most." So I think it actually
comes down to like what excites you the most? Is it the doing
the analysis and like giving the business the answer and having less meetings
and being in the weeds and knowing the data really well Or is
the bit that really excites you, the like helping a junior person learn
how to do their first presentation or explain a technical methodology really

(35:47):
well to a stakeholder or like, I mean obviously management also comes with
all of the stakeholder stuff, which is a whole nother separate thing.
But I think you need to figure out which bit you're most passionate
about. And I'm obviously very passionate about the team stuff
much more than I am about the doing the work myself.
Although sometimes I still get kind of sucked back in. 'cause like it

(36:08):
is a bit of a high right? Like we wouldn't work in data,
we didn't love that high of like, oh, look at this amazing analysis.
But like the reality also is if you're a person who,
someone once said to me, they're like, oh, they were in their first
management role. They're like, "this feels like a lot of politics."
And I'm like, it's not politics. It's about figuring out

(36:29):
the best way to communicate something that you help the company make the
best decision possible with the information that you have, that might feel
political to you, but that's not how I describe it. So if you
don't have the stomach for that, also probably not a manager type person.
People Who note politics, I'm like that's either, they're either very cynical

(36:50):
and they're gonna... They need to adjust their perspective, which I think
you just did a great way to kind of reframe that.
It's possible they're at a company that actually has, if it's that dysfunctional,
I mean, not that there isn't some level of some aspect of gamesmanship,
but if that's your framing, like whether it it is politics or whether
you're perceiving it as politics, Yeah. You're not gonna work out well on

(37:15):
either one, because I don't think you wanna work long term unless you
are a direct descendant of Machiavelli. I think it's really tough.
Like I know now deeply and have known for
years that I am an IC for life doesn't mean I don't like
collaborating with people, doesn't mean I doesn't like coaching and providing

(37:37):
some support and guidance. But I don't know that I would
know that as deeply as I do if I had not
been in a management role. So I could see, I don't know that
I've seen this happen. I don't think you really know until you try
it. So if there's an opportunity to say, put me in a role
that is somewhat supervisory or managerially, but I am gonna put

(38:02):
a 12 month timeline on it and somebody is gonna hold us accountable
to have a discussion about whether I wanna
continue with this or whether I want to go
kind of back IC. 'Cause there are people who a year in,
they're like, "This is amazing. I know I will be
satisfied for the next decade of my career because I'm getting so much

(38:23):
energy from this." And there will be people who say, "I'm waking up
every day and spending the first seven hours of my day getting to
the last hour when I can go and actually just do some work
that I'm really excited about." But I think it's hard
to know. So the extent that you can
try it out in a way that you don't feel like,
I mean, you're never committing. You're never like,

(38:45):
"Oh, if I go down this track, there's no going back."
'Cause it's not a going back, it's taking a step over
to the lateral path of continuing as an IC.
And I have two people on my team that just did that,
that were leading teams. And then we've had a conversation and they've gone
back to both being senior ICs and they're absolutely killing it.
I also think that just because you tried at one place,

(39:07):
also keep in mind the context, you might be in a position to
give it a try later because maybe there's something about that context that
makes it unappetizing, but don't close it forever. 'Cause I totally agree
with Tim. You're not pouring concrete, whether you switch roles in your
current company or switch to a different organization, it's all fluid.
But the one thing I will say, you never know until you try
it. But Moe, I thought you made a really good point when you

(39:29):
were saying, "Do you really like explaining things to stakeholders that's
really complex or training or coaching analysts?" So you think like,
"Oh, I've never been a manager before." But think about some of the
elements that would become more central to your role because
it's likely that you've actually dabbled in it. Like maybe you had an
intern working for you last summer. Did you enjoy that experience?
And did you enjoy when they saw success and some of those things

(39:51):
to give you like that little inkling? But the one thing that I
will say that I hear a lot when this like divergent path conversation
comes up is the only way to move up is if I manage
a team. And I will say that at least in the past couple
of years, I feel like so many organizations have given a more senior
path to success on the IC side. So if you've hit a ceiling

(40:12):
at your current organization and you really feel pulled to the IC side,
maybe it is time to look outside your organization because there is absolutely
a path, staying IC where you can move up in seniority and your
scope and the type of decisions that you can be helping influence within
the business. So just wanna make sure that that gets out there too.

(40:32):
That's a great point. Plus one, move companies if there's no path to
seniority in IC. 'Cause like we have an IC track and a coaching
track and like you can get the same pay or the same
benefits of being a senior person and your complexity definitely grows with
that as well or the problems that you're solving. But yeah,
then if that's the route that you wanna take, move to a company

(40:54):
that will have a path for you as an IC. It's like canva.com/jobs. Slash
Moe Kiss referral code. Yeah. It's also I think really important.
I think we've sort of touched on it to just recognize the complete
difference in the skill sets required, right? So managing your own efforts
versus starting to take on and managing the efforts of others.

(41:16):
So if you transition into a management role,
you should expect a lot of learning and building of skills,
which is by its nature confusing, disorienting and frustrating. And so I
think that's the other thing is give yourself time to grow
as a leader, as a manager. If you take that on and don't

(41:38):
necessarily say, "Oh, in my first year it was rough, I felt disoriented,
throw it away." It could be that as you kind of evaluate both
kind of your personal skill sets, desires, those kinds of things in your
personality also factor in. It may take you a couple of years to
really start to build the skill sets necessary to do that.

(41:59):
A lot of times when I've led teams, I'll say managing people is
not for everyone and you should work really hard. You deserve it and
your people deserve great leaders. And so don't just jump into a management
role if you know that it's not right for you or you know
you don't actually wanna do it. You'll hate it. Your people won't be

(42:19):
benefiting from it. And so nobody benefits. And so it's tricky,
but also if you do decide to go into management, make sure to
give yourself time to grow in and learn the skills because it's
nothing like what you've done historically. Do you think though after...
I mean, I pulled a year out of my ass, but do you
feel like after a year, you'd be like, "Oh, this is,

(42:39):
I'm getting fulfillment. I just have skills to develop and I should continue."
Versus, "Wow, this is." Well I think... Yeah. For myself, and again,
I would rate myself as somewhat slow learner.
I started to feel like I was doing a decent job as a
manager, probably about three years in, and those first two years,

(43:01):
I was definitely struggling at learning, but I feel like you're always gonna
be learning. And then I started reporting to you and I tried to
undo all that. Yeah, that was kind of the nail in the coffin where
I was like, "That's it, I'm out. I don't wanna work with nobody
anymore." I think a good question to ask yourself too or something to
research in your decision is understanding what does adding value look like

(43:23):
to the company? Because it's a very different mindset. I think as an
individual contributor, you're very used to having a lot of control
and you have a very physical output to say, this is my value
to hang my hat on. And I've experienced a little bit of this
and had some conversations around it. It's a big mind shift and a
new mindset to have when your value add is influencing others or letting

(43:47):
them have that very physical success of a deliverable or something or hands
on keyboards. And so sometimes you can feel like, "What am I doing?
I was busy all day in meetings, talking to people and things."
And then you're like, "I don't know what I have to,
"show for it." And so I think you have to be really honest
with yourself of like, my expectations and value in this new role would

(44:08):
be this whole list of things and not what I'm used to as
an individual contributor. And that can be a part of that pivot,
I think, Michael, you're talking about. So maybe thinking through that early
on would help you make the decision too
of would I be fulfilled by trying to add that value instead of
the individual contributor. And the funny thing is, Julie, I have this a

(44:28):
lot with people who are managers that are going for promotion,
because as part of the promotion pack that we put together,
you have to explain your achievements. And people are like, "I don't want
to take credit for my team's work." And you're like, "I'm not telling
you to take credit for your team's work. What I am saying is
describe the project and your role, which might have been like identifying

(44:52):
the resources, who in the team is gonna work on it.
It might've been getting the stakeholder buy in. It might've been helping
the team refine like next steps and that sort of thing."
And like your role, you did play a role in that project.
You might not have been the technical lead. It's not taking credit for
your team's work, but you just, the way that you get comfortable with
describing your value is understanding that those things you're adding now

(45:16):
may look different to what they looked like before, but it was still
very important in helping the team deliver that project successfully. And
sharing the story of their wins and being able to put the spotlight
on them and pull out those best parts. Yeah. Absolutely. Yeah.
I would say as a leader, your people's wins are your wins.
All right. Let's do one more before we wrap up. So let's, this

(45:39):
one came in." When I'm interviewing for an analytics position, what are
some of the questions I should be asking the interview that will help
me determine if the position is one that I should take if I
get offered it?" Oh, that's a really great question.
One question that I got asked during an interview, and it was for

(45:59):
more of a leadership position in data, but it... I felt like it
was such a solid question because the reality is when you get asked this
as well, you're gonna answer, well, I felt my intuition said to answer
it honestly, which was "how are decisions made at this company?"
And when you think about it, it's a very open, broad question,

(46:24):
but you can go in so many directions in answering it,
right? But one is the company kind of data informed in their decision
making. The other one is like, is there a particular specialty that leads
decision making? So like it's our product managers or our engineers or our
designers that really drive decisions. Is it a founder led company?
And that's how decisions are made. You actually somehow kind of get the

(46:46):
dirt on the company and get a really good understanding of
what you're gonna have to navigate by asking that question.
So that was one that I'm probably always going to ask now if
I'm interviewing myself. That's a really good one.
I think that throughout the interview process, hopefully you've gotten a
sense for the culture based on the different people that you're talking

(47:08):
with or what the responsibilities of the job will be. So if that's
all taken care of and you're solid there, I think making sure that
two things that I've learned are very important to me is making sure
that your manager is gonna be there to be your support person/
shit umbrella taker for the stuff that comes down from above,

(47:29):
right? So maybe you were gonna ask some questions about when a difficult
situation arises or when we're dealing with a budget cut, how will we
make sure that our most important projects are continued to stay resourced
or asking some little bit scenario based questions because making sure that
you are in lockstep with your manager, there's... You can put up with

(47:50):
your plus two, you're not really seeing eye to eye with or not
getting along with. You can put up with some peers that aren't great,
maybe even some direct reports that are thrown in your side.
But if your manager doesn't... If you don't feel like they're gonna be
someone who's in your corner advocating for what you need to be successful,
it's gonna all fall apart. You could be working at the most inspiring,
amazing company that's aligned with your missions. And if that's broken,

(48:11):
there's nothing that can repair that. And then I think the second thing
too, is if you sit down and write down a list of things
that are really important to you in your role. One of the things
that I learned doing that exercise most recently is it wasn't necessarily
about the size of the team or... Wait, hold on. Wait,
how recent? How recent? Wait, is there something I need to know?
Cannot disclose. The last time I did this exercise, which was a couple

(48:36):
of years ago, that previously it was all about what is the remit
or what team would I be supporting or what type of company or
what is the size of the company? And the last time I did the
exercise, it was way more about, do I feel inspired by the work
that I've been doing? Do I feel like my manager supports me like
the one I just touched upon? But again, making sure that you understand,
like Moe said, how decisions are made. That's such a good one.

(48:57):
I think how the work gets done, how are we gonna build our
budget together to make sure that the resources that I have,
whether it's teams, technology, the support cross functionally, it's just
really digging into what's that gonna feel like so that you're going in
as eyes wide open as you can. 'Cause it's, it really,
the nuances of, must have six years of experience in whatever like, throw
that out the window. It's once you get there, it's really more about,

(49:20):
again, the value delivery and the relationships you can build to do cool
shit. So I think that those are two things that I always probe on. Is that
second one a better one to try to get from the hiring manager
or if you've got someone who will be a peer to ask them
or is it both? Good point. I mean, whoever seems like they're the
straightest shooter. I don't know. Yeah, there you go.

(49:41):
Yeah. Someone who's not feeding you the company line. I would ask too, trying
to understand like your support structure. So depending on the role you're
going for, like if you're more middle of your career, let's say,
and you still wanna learn a lot while you're there and you want
mentorship or you really like collaborating with peers, I think asking questions
about understanding the size of your team, who else is on the team,

(50:03):
maybe people that have similar knowledge sets to you so you could learn
from them or trying to understand, does your team partner with other teams
in the organization? So then you kind of go in knowing,
are we really siloed and nobody talks to us and I'm really gonna have
to create inroads or are there things in place and relationships I can
lean on to make me better and ready to go at this role?

(50:25):
I think for me, that's something that would be always top of mind
to try to ask about. I think, Julie, you're... I mean, this is
like both Julie and Val's points smooshed together, but one of the things
like, I do actually also have criteria that I rank,
that I've worked out are important to me in a role and I
rank them. And the last time I was job hunting, I had a
few different opportunities and I was trying to figure out which one to

(50:46):
take over a different one. And I basically scored the things that were
important to me in like a matrix. And one of the things that
was really important to me was people I could learn from.
Now, I don't think that is necessarily something that you're going to be
able to answer with a question, but sitting down before and knowing that
was important to me, I walked out of one interview where they had

(51:08):
asked me, they basically expected me to be able to rote learn R functions
and describe them to them. And I was like, "That's not me."
When I use an R function, I'm gonna look it up and understand
how it works. That's where my level of R programming is.
Another interview I was in, which was my Canva interview,
I had three people, we were working through some R code and one
of the guys was like, "Have you ever used this function?"

(51:29):
I was like, "No, I haven't." He's like, "Let me show you how
it works." And I walked out of the interview being like,
"Oh, I learned three new things in R today."
If that's what it's gonna be like being in this team every day,
that's the team I wanna be in. So it's not always necessarily having
the perfect question, but it is spending the time and giving thought to
what's important to you and how you're going to assess that through an

(51:52):
interview process. Very smart. I think first and foremost, you wanna determine
what the coffee situation is. Oh, that is true. If
you don't have decent quality coffee provided by the company, do not work
there. But Michael, this is a remote position. Oh, well, that's a little

(52:12):
trickier. Remote, you have some benefits of being remote, blah, blah, blah.
Anyways. No, I thought that'd be a great question to ask.
So one. I'm not sure you wanna just observe that or just ask
that. If you have an over lunch interview, that's a good time to
bring up the coffee question. But you definitely should know, and I'm saying
it jokingly, but actually there's some seriousness behind that, which is

(52:34):
little things like that will indicate level of investment.
On a more serious topic, I would try to determine the disconnect between
senior management and the analytics team that you're joining. And where
is it? And so asking questions of the management team, what is the
biggest challenge that you're facing? And then comparing that to what the

(52:54):
analytics team tells you will tell you how much disconnect there is between
management and analytics. And that's a really, really key thing for you
to know going in. The other thing I would try to understand is
to what extent is the analytics organization trusted and brought in?
Because if there are other parts of the organization spinning up their own

(53:15):
analytics and data, you're walking into a sea of bullshit that you wanna
avoid, to be honest. So it's sort of like, "Oh yeah,
well the sales org kind of does their own thing over here because
they don't trust any of our data." Red flag, red flag,
red flag. And maybe as a leader, you're coming in to change that
culture, but know what you're getting into. And if you're an individual

(53:35):
contributor, know that that's gonna pull against your ability to do your
job in a meaningful way in an organization that's gonna be splitting itself
up and having internecine battles between different groups of the... Different
parts of the organization. So those are two parts that I would.
That's so good. And that last one. Such good lines. You should also
be wary if it's like, "Oh, we have this centralized analytics team, but
they never really give me what I want. So we have this role

(53:57):
to just sit on our team." Like that's the same red flag,
it's just the other side. Yep, yep. You're getting hired to basically work
against the organization, which has some benefits depending on how powerful
the person is within the organization, but know you're walking into a war.
And that means if you need a data warehouse set up or review set
up, you may be going into enemy territory to work with the data

(54:20):
engineering team to do all those things. So
it's all these things, but anyways, hopefully that's helpful. Apply for
a job right now. Mr. Wilson. I'm ready to go. I've never been
more equipped. I'm feeling fired up right now. Yeah, I'm used to these
tips. I knew that was gonna be a really good one.
I was excited about those answers. This has been great. And again,

(54:44):
a huge shout out and thank you to our listeners for bringing those
questions to us through the listener survey. It's so great to hear from
our listeners and get these questions. So really appreciate it.
And thank all of you, Julie, Tim, Val, and Moe for your expertise
and taking the time to answer some of these.
And thank you, Michael. Oh, well, I was waiting for that. That was

(55:07):
what I was waiting for. Val read it right. I feel like I
threw the fishing rod out there and then just, anyone? Yeah, and also
just a huge shout out to Ken. Oh, Ken Riverside and the whole
Fourth Floor production. I mean, we'd be remiss if we didn't thank them.
Huge in the Chicago podcasting scene. All right, listen, obviously we also

(55:30):
wanna give a big thank you to Josh Crowhurst, our producer.
He does so much behind the scenes to make the show possible.
So thank you, Josh. As I look back on sort of this topic
and some of the questions we answered, the career journey that we all
face as analytics professionals is definitely complex, but
I think there's more clarity now and more help now than there's ever

(55:52):
been in this. And so hopefully you're feeling encouraged and hopeful about
that. And we'd love to hear from you if you have more questions
or more discussion. And there's great ways to do that. We've got a
lot of presence on the Measure Slack chat group. As well as a
LinkedIn page and our YouTube channel, which you go to YouTube and search
for Analytics Power Hour. You can find that and please subscribe.

(56:14):
We just got that up and running with all of our old episodes
recently. So you can find episodes there on YouTube as well as on
your favorite podcasting platform. So I know I can speak for all of
my co hosts when I say no matter what your role,
no matter if you're in management or an individual contributor, wherever

you're at in your career journey, keep analyzing. 0:56:39.5 (56:34):
Thanks for
listening. Let's keep the conversation going with your comments, suggestions,
and questions on Twitter at @analyticshour, on the web at analyticshour.io,
our LinkedIn group, and the Measure Chat Slack group. Music for the podcast

(56:54):
by Josh Crowhurst. So smart guys want to fit in. So they made

up a term called analytics. Analytics don't work. 0:57:04.3 (56:59):
Do the analytics
say go for it no matter who's going for it? So if you
and I were on the field, the analytics say go for it. It's
the stupidest, laziest, lamest thing I've ever heard for reasoning in competition.
This podcast studio is costing me money. So let's get this show recorded

(57:23):
now. Yeah, also, why are you in a podcast studio? Okay, because lightning
struck my house. 'Cause his house is still on fire. And I have
no internet. I have no garage door openers.
And yeah, my computer is half broken. My brand new microphone is dead. Georgia's
infrastructure, there's still... Yeah, I have a feeling like my house is

(57:46):
some sort of like electric Faraday cage thing where it's like just hit
me with lightning all the time. The wires. Can you put some like giant
trees next to the house? And maybe they'll just, deflect. I do have giant
trees. No, there's trees all around my house. They missed it and they hit
my house. And your ear. And my ear. My motherfucking ear. How's that for

(58:17):
an outtake, Josh? I never got confirmation from Tim on my eyes,
so just crossed my fingers. Val and Julie, both of your eyes are
better now like after Tim gave you that feedback. Yeah,
I do it during internal meetings. Good. Yeah, me and my wonky eyes. Facts
and feelings and your eyes are two thirds. Type A, type A personality.

(58:43):
Oh, I didn't even put any conditional formatting after you changed it to.
What? Yeah, you slipping. No conditional formatting? You're slipping. Did
you manually color those? Did you get hit by lightning? One of us did.
No, people get Michael and me mixed up a lot. I'm gonna bring

(59:04):
a bunch of eggshells and just scatter them around your feet so people have
to walk on eggshells around you. Rock Flag and McKenzie titles.