February 11, 2025 • 31 mins
In this episode, I chat with Daliana Liu of The Data Scientist Show! She talks about her career journey, including her tenure at Amazon, and offers practical advice on making data science impactful in business. Tune in to discover what truly makes a great data scientist and check out Daliana's Data Science Career Accelerator course, designed to help data scientists advance their careers: https://maven.com/dalianaliu/ds-career
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⌚ TIMESTAMPS
00:00 - Introduction
13:55 - Focusing on non-technical skills
18:07 - The importance of communication skills
23:11 - How to have positive visibility in your company
28:25 - Data Science & ML Career Accelerators
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🎥 YouTube Channel: https://www.youtube.com/@UCa0RTSXWyZdh7IciV9r-3ow
🤝 LinkedIn: https://www.linkedin.com/in/dalianaliu/
📸 Instagram: https://www.instagram.com/dalianaliu/
Website: https://www.dalianaliu.blog/
🔗 CONNECT WITH AVERY
🎥 YouTube Channel: https://www.youtube.com/@averysmith
🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/
📸 Instagram: https://instagram.com/datacareerjumpstart
🎵 TikTok: https://www.tiktok.com/@verydata
💻 Website: https://www.datacareerjumpstart.com/
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Daliana Lui (00:00):
I think a lot of times we think it's important to
constantly grow your technical skills,but that only get you somewhere.
So basically, if you imagine the careertrajectory from junior data scientist
to senior data scientist and later staffand principal scientist, you'll see the
requirement for technical skills slowly.
(00:20):
The increase is there.
Not that high.
And however, it requires morecommunication skills, leadership
skills, influencing skills, higherlevel you want to, um, become.
Avery (00:32):
So Dalyana, you have almost 300, 000 followers on LinkedIn.
You're a LinkedIn top voice.
You're the host of thedata scientist show.
Uh, you've worked as a data scientist,uh, at Amazon, and now you're kind of
doing your own thing, teaching otherpeople how to be data scientists.
Thank you so much for comingon the podcast and, uh, I'm
so excited to have you here.
Daliana Lui (00:51):
Yeah, thanks for having me Avery.
It's been a long time coming.
Avery (00:54):
Yeah, I've been on your show and now you're coming on, on mine, but
I'm really excited for, for me to getto know your story better and also for
our audience to know your story more.
And also just know more aboutlike what it actually takes to
be a data scientist, you know,specifically at a company like Amazon.
So for those who maybe haven'tfollowed you in the past, can you
just give like a quick overviewof what your career has been like?
Daliana Lui (01:17):
Yeah, so I started studied applied math in college when
I lived in China, and then I felt itwas too much theories, and I wanted
to learn something more practical.
So that's when I startedto get into statistics.
Got my master in, um, University ofIrvine, University of California.
(01:40):
Uh, and then I got my job as a businessintelligence data, data analyst.
So at that time the word data scientistwasn't invented, but I was basically
doing, uh, data science generalist work.
Uh, I'm doing.
Analysis for the marketingteam, build time series model.
And then I, uh, got into Amazon.
(02:03):
I moved from LA to Seattle.
Uh, my title was again, businessintelligence engineer slash statistician.
I think that's a.
Basically perfect kind ofrole for a data scientist.
Uh, and later I work on experimentation,AB testing, product analytics.
(02:24):
So that's the first few years in Amazon.
And then I got into machine learningand deep learning and moved to,
um, Amazon web services and alsogrow to a senior data scientist.
Avery (02:36):
Very cool.
And I think it's so interesting that youstarted your career as a data analyst
or like with that data analyst title.
Do you recommend that for others?
Like starting as a data analyst or wasthat role already kind of a data scientist
role, but just had the title of data.
Daliana Lui (02:51):
I think today, if you look at what people do under a data scientist
or data analyst role, it's so different.
For example, at Facebook, thereare product data scientists.
Don't do much machinelearning and modeling.
And they write a lot of SQL and theyprobably also use Python and in some
(03:12):
other companies, there might be adata analyst also doing some modeling.
So I would say it really depends onthe company and what a role specifies,
but in general, data scientists do.
Use Python more, do a little bit moreautomation compared to data analysts.
And some data analysts, theywork more as a business analyst.
(03:35):
They work closely, veryclosely with, um, stakeholders.
I don't think there's a good or bad tostart your career where it really depends
on what you're interested in, what kindof job, uh, market look like, and, uh,
uh, Regardless of where you get started,you can grow and become either a manager
(03:56):
or a principal data scientist and analystin your, uh, in your career track.
Avery (04:01):
I agree that there's probably not a right or a wrong necessarily,
but one thing, one thing that is reallyinteresting is just that those titles
kind of being all over the place.
And I think that's true today.
I mean, I think it'sprobably more true back then.
Then, but even today, like I see someof the strangest titles, like I've
seen a data science analyst before,uh, or data analytic scientists.
Yeah.
And I'm like, I don't know exactlywhat, what those roles are.
(04:23):
So if I had to ask you,what is a data scientist?
What would you say the definitionof a data scientist is?
Daliana Lui (04:28):
Yeah.
I think the data scientist is someonewho uses data and some kind of framework.
Could be experimentation, could be,uh, machine learning, or it could
be some kind of statistics analysisto help their usually business, uh,
stakeholder make better decisions.
(04:49):
Um, and this decision could be onedecision could be you automate a million
decisions, making it into a machinelearning model and eventually have
some sort of business impact, meaningit help your company make more money.
Uh, save more time, save money, et cetera.
Getting more customers.
Avery (05:06):
I like that definition.
Do you, do you think that like, doyou think that there's a difference?
Cause like a data scientist is kindof what you just explained, right?
And there's this whole fieldof study called data science.
Do you think data scientists arethe only ones that do data science
or like, where do you see datascience versus data analytics?
Daliana Lui (05:24):
Yeah, I think now everybody does data science, not just data
analysts, product managers, they haveto know some data science, data science.
They might not be the onethat always writes SQL, but
they need to understand it.
And I've also seen a lot of automatedanalytics or machine learning tools.
(05:44):
Maybe a product manager in thefuture can easily use those
tools to create some analysis.
Engineers.
They need to know data science.
In fact, a lot of AI engineers thesedays, they basically came from a
software engineering backgroundand then they learned machine
learning statistics on the go.
And of course, we'lltalk about the overlap.
I think the biggest difference isin general, I would say, See the
(06:08):
data scientists, the people witha data scientist title, um, some
of them work on machine learning.
Some of them don't, but the ones whowork on machine learning, deep learning
have more engineering element in it.
See them more often ina data scientist title.
And for a data analyst.
I think the data analysts today also doa lot of automation, but it probably lies
(06:29):
in, uh, some of them might do some dataengineering work or creating a dashboard,
new automated dashboard, but it doesn'tmean their work is easily automated.
Need to communicate a lot withtheir stakeholders to find out
what is the most important thing.
What's the story you need totell from their, uh, dashboard.
Uh, so they probably use moreSQL and some data analysts.
(06:53):
I know they use a lot ofExcel as well because their
stakeholders are not technical.
Avery (06:58):
I think that's like a good definition because really at the day.
There is so much overlap.
Um, and it really, like yousaid, depends on the company.
So it, it's, it's quite difficultto, to actually draw a line on, uh,
let's talk about some of the workthat, uh, you've done in your career.
So as a data scientist, you, you mentionedthe term machine learning, which, which I
think is a term that a lot of people hear,but maybe don't know the definition of.
(07:21):
For, for me, it's basically justusing some sort of, of math to
accomplish some business problem.
The biggest one probably is predicting,uh, what's going to happen in the
future, but there's obviously otherthings like, you know, separating
things into groups and stuff like that.
Would you say that's kind of a fairdefinition of machine learning?
Daliana Lui (07:40):
Yeah, I think so.
It's basically learning machine learningis basically learning patterns from data.
If you think about a pattern oflet's just say you drink coffee on
Monday, Wednesday, Friday, but don'tdrink coffee on the rest of the day.
So if I have enough data, if youfollow those patterns, say 90
(08:01):
90 percent of the time, I'm ableto use a model to learn that.
So I think that's the.
Simplest machine learning probablyjust have like one parameters,
which is the day of the week.
Uh, and today, when we think aboutmachine learning, it's more complicated.
We're probably some models.
If you think about the open AI hadGPT probably have, uh, I don't know,
(08:22):
millions, billions of those parameters.
Avery (08:26):
Yeah, that that's pretty complicated stuff, but you've also
worked on some pretty complicated stuff.
I would imagine at Amazon, one ofthe things that I saw that you, you,
you'd kind of co published with Amazonwas essentially a soccer project,
which I played soccer growing up.
So I was a big fan.
Yeah.
And I think a lot of people listeningreally, really enjoy sports.
(08:47):
Maybe they've seen the movie Moneyballor read the book Moneyball kind
of highlighting the Oakland A'sand how they used analytics to,
to, you know, win a championship.
Uh, can you talk a little bit aboutwhat you kind of did at Amazon
with, with this soccer project?
Daliana Lui (09:00):
I was at Amazon Web Services and, uh, our team at that
time was called ML Solutions Lab.
So basically we're a group of consultants.
We help AWS customers, um, implement amachine learning, deep learning solution.
So this customer came to us.
They are a sports betting company.
They have a lot of soccer game dataand they want to see whether they
(09:23):
can predict whether there will be asoccer goal in the next few seconds.
for joining us.
And so this is the first computervision project I worked on.
And it's a very complicated projectbecause we need to analyze the videos.
Uh, we basically, um, used a few differentframeworks to chop the data, um, into
(09:46):
kind of five second, five seconds, sevensecond clips, and then we have to manually
Manually label the data into whether thismoment is a goal, whether it's not a goal.
And if you watch soccer, youknow, sometimes a very intense,
uh, how do you call it?
Uh, attack.
It looks very similar to a goal.
(10:07):
So we also need to label that to traina model, to learn this is attack.
This is not a goal.
So a fun story is becausethe data came to us.
We're not labeled.
So me and my coworker spent two days.
Just looking at those clips to labelwhether this is goal or it's not a goal.
So over the two days, I think I probablywatched hundreds of soccer goals.
(10:31):
I don't want to watch soccerfor the next couple of years.
So that's the unexpectedpart of data science.
Sometimes you need to do a lot of thosetype of data quality check, labeling.
But you have to do those type of thingsbecause we, we label it in a very specific
way that we know how to train a model.
Eventually, uh, we used, uh,we experiment that on a few
(10:54):
different, uh, video analysis,uh, modeling called, uh, um, I3D.
So basically it's an inflated 2D.
Using, uh, inflated 2D modeling toanalyze, uh, the data and the way,
how to simplify the business problem.
Because like I mentioned, aftertech, there could be a goal.
(11:14):
It could be not a goal,maybe ended in like a corner.
For example, we'll just simplifythat into a binary problem.
That's also, um, important way to tacklea very ambiguous, complicated problem.
Sometime you might not, youmight need to reduce the scope.
Uh, for example, this, in this case,we reduce the problem space from a
(11:36):
multi class classification probleminto a binary classification problem.
And then we train a model.
When we came out with a classifier, uh,with a classifier, we Use a classifier
to run through the entire game.
Every five seconds, we run through thatclassifier and then see whether there
(11:57):
will be a goal in the next few seconds.
We also created a very fun demo.
Basically, in real time, you can see aUh, likelihood score of whether there
will be a goal in the next few seconds.
It can make the viewingexperience more exciting.
Avery (12:13):
Yeah.
I saw, I saw the demo actually, andmaybe I'll, I'll insert a little
recording because it was pretty cool.
Um, but what, what a cool project.
Uh, and I think.
Uh, there's so manydifferent, different things.
I think that listeners can, can learnfrom that one companies like Amazon.
And honestly, a lot of companies are actas consulting companies a lot of the time.
Uh, and so what, like a gamblingcompany in this case, or any other
(12:35):
sort of manufacturing company,or I don't know, whatever.
Company that exists a lot of the timesthey like kind of outsource their
analytics and data structure stuff tosmarter companies like, like Amazon.
And I think that's, that's good to know.
And I think that's a cool role to sitin is basically you get to do analytics
for, for multiple, multiple companies.
I think that's really cool.
And then the other thing I love thatyou said was, you know, I didn't seem
(12:56):
like you were too much of a soccer fannecessarily, and you to become one.
Uh, and that's sometimes what youhave to do is like, you maybe don't
have the domain experience, but.
You can kind of need the domainexperience when you're building machine
learning models a lot of the time.
Daliana Lui (13:10):
Yeah, exactly.
And I also worked on a football, Americanfootball project when I was on the team.
I knew, I mean, I, I know a littlebit of soccer, of course, but I knew
nothing about American football and Ihave to buy a book to read how football
work, uh, to, to, you know, to ourpoint, to understand the context.
Avery (13:30):
It's crazy because yeah, it's just, there's the cool thing about
analytics and data science and machinelearning is it's really industry agnostic,
which really means that you can takethe principles, the machine learning,
um, models and the machine learningalgorithms and apply them to really
so many different business problems.
And so you could probably spend yourwhole life just learning about different
industries and how to apply just one modelto those, those different industries.
(13:53):
Uh, which I, which I think is fascinatingand one thing I want to give you
credit for in your LinkedIn content.
A lot of the times you're,you're obviously very technical.
Like you use a lot of very fun buzzwords,uh, when you're kind of explaining that
and you've obviously worked for Amazon.
So you're obviously very technical, butone thing I really appreciate about your
LinkedIn posts is, you know, sometimesthey're technical, but other times they're
like, Hey, you as a technical personactually kind of get more done when
(14:17):
you focus on your non technical skills.
Has that been true for you in your career?
Daliana Lui (14:22):
Yeah, absolutely.
Uh, I think a lot of times wethink it's important to constantly
grow your technical skills, butthat only get you, um, somewhere.
And after that, uh, I wishI could show you a plot.
So basically if you imagine the careertrajectory from junior data scientists
to senior data scientists, and laterstaff and principal scientists, you'll
(14:45):
see the requirement for technical skills.
Slowly, the increases.
Not that high, and however you require,it requires more communication skills,
leadership skills, influencing skills,higher level you want to become.
And I think once we get into thereality, there's no homework anymore,
(15:07):
and there's no, uh, perfect data.
And a lot of times the stakeholders arenot even clear about what they want.
And so it is essential to know.
You know, from the beginning of theproject, how to ask the right questions,
how to work with the right people,how to find a project that actually
have the high impact that can get youa promotion and later on, how do you
(15:28):
influence the right stakeholders toget your solution in the right place?
Avery (15:33):
It's a, it's a crazy concept because I think we, we like to think
as technical people, the, the moretechnical you are, the more you'll get
paid, the more desirable you'll be, themore influence you'll have at a company.
And, and to be honest, it's just not true.
Even if you're not junior levels,even when you're trying to get hired.
It's not like the smartest person orthe best person at SQL lands the job.
(15:55):
There's often these soft skills, thesepeople skills, these communication
skills, uh, that come into play and,and really kind of make the difference
between maybe a good data analyst, agood data scientist, and a great one.
Um, one of the ones that you posted aboutrecently, and I think you kind of just.
Hinted at it just barely.
And your answer was sometimesthese stakeholders don't have
a clue, uh, of what you want.
And so one of the things youposted recently was like, one thing
(16:17):
that can make you a great datascientist is getting feedback early.
Can you expound on that?
Daliana Lui (16:22):
Uh, when I started, uh, in Amazon, I wanted to show
my manager where my stakeholders,my work only one is perfect.
Otherwise I would feel embarrassed.
But reality is.
Sometimes you think you understandtheir request, but you don't.
Or during the time when you're workingon a project, their preference,
(16:43):
their priority have changed.
So it's important to constantlyalign with your stakeholders to make
sure you understand their needs.
And also, there's only It's verylimiting what you can communicate
three words, especially you'reworking on a data science project,
whether you need to turn that into adashboard or machine learning model.
(17:04):
So you have to show them your demo.
Um, I, in my career growthcourse, I always talk about
show them a ugly demo first.
Even if it's just in your, um, Jupyternotebook or in your, you know, SQL,
uh, you know, editor, show them to letthem know what's the, uh, what does
(17:24):
the MVP look like, it's even betterif you can create a very small UI
so they can play with, they can getexcited for, and when they see what.
It might look like it gave them more idea.
So it's not a bad thing when they tellyou, Hey, this is not what I want.
If you're only 20 percent of theproject, but it will be a huge problem.
(17:46):
If you're already at 80 percentof projects, actually you want to,
uh, have those small tweaks and askthem, Hey, is this what you want?
Or I have a few other ideas thatI think that might help you.
This is my proposals.
What do you think?
So have those conversationsearly can save you a lot of time.
When you're towardsthe end of the project,
Avery (18:07):
I'm sure, I'm sure you've seen this now as you've grown in your career.
And I've seen it as I've grown in mycareer to the point now where I, you
know, I code a little bit, but a lotof what I do is, is directing other
people to code and stuff like that.
And I realized that, you know, now I'mthe stakeholder and I've become the, a
bad stakeholder where I don't even knowwhat I want half the time, what I'm
asking, or when I do know what I want, Ikind of stink at explaining what I want.
(18:31):
Um, and so when people.
You know, who, who are workingunder me, are able to come back
with something quickly and be like,Hey, is this what you're asking?
Uh, especially like in a meeting or ina demo, like a loom video, uh, because
I like, like what you said, you,you can only say so much with words.
It's almost like, likeinternet speed, right?
That's basically like how fast informationcan transfer words is like, I don't know.
(18:52):
15 megabytes per second, butlike an in person meeting,
we're talking like gig speed.
Um, there's just so muchmore communication, which is,
which is, which is awesome.
And that ultimately leads towhat, what's called like adoption
and people using your analysis.
Um, and that's another thing that you'vementioned, uh, on LinkedIn that like.
You can't really do datascience just for, for funsies.
(19:14):
You have to get it adopted.
Can you talk a little bit more about that?
Daliana Lui (19:17):
I think there was a data point a few years ago, probably over 80
percent of machine learning models fail.
I think part of it is naturalbecause there's a research or
discovery nature in data science.
Not everything has tobe put in production.
But a lot of times if Whatyou have done become useless.
(19:39):
Then from the company perspective,they wasted their time.
You don't have direct impact.
And from a personal growth perspective,if you don't have the impact, it's
hard to define your contributionto the team, to our growth.
And how do you advocate?
Yourself for that promotion, whenyou build something, a lot of data
(20:01):
scientists and also engineers, they wantto just build something that they think
is important or they think is cool.
They just learn some modelfrom a Coursera course.
They want to implement that.
I think that's a great way to learn.
By doing, but when it comes to, um, doingwork for a, most of the time for profit
company, you need to think about is whatI'm working on aligned with my team goal.
(20:26):
I'm not helping my stakeholderor, um, this is the five goals.
My manager tried to achieve.
I'm not helping my manager.
I might be a team player.
It's better if you can alignyour passion to the impact.
And sometimes the passion andimpact might be a separate thing.
There is one thing maybe you can exerciseyour passion for, for learning on your
(20:48):
own time or take 20 percent of, you know,your, your work time, but make sure the
80 percent of your time, you're actuallysolving the useful business problem.
And a lot of time, itcould be a little bit, um.
Boring and repetitive.
Um, I think that's also an opportunityfor you to create more impact, to
see how can you, um, automate this?
(21:10):
How can you also, sometimes you need tomotivate yourself that again, aligning
with the stakeholders, with the customer'spain point, stakeholder's request.
Sometimes if you see how does thatimplement it, how it solve even
just one person's problem, it can.
Also make you feel more motivatedto work on projects like that.
Avery (21:30):
It's hard because in, in data, especially like in school, right?
Let's just take like a normal, you know,college, maybe like a master's degree
or maybe even an undergrad degree.
Your master's was in what again?
What did you say your master's was in?
Daliana Lui (21:42):
In statistics.
Avery (21:43):
Okay.
And yeah, and my master's was in,was in data analytics technically.
Right.
Um, but like, I would imagine itwas the same in your master's, but
my master's was very theoretical.
Um, and it was all about like, like, forinstance, you, you might be interested in
statistics of, of like getting a P valueless than, you know, zero point or 0.
05.
(22:04):
And you might be interested in like, okay.
Like, can we make the P value lower?
I know we can't really make P valueslower, but like you might be interested
in really low P value or, or inlike my masters, it might be like,
Hey, we're like 79 percent accurate,can I get to 81 percent accurate?
So we're thinking in likeP values and percentages.
But really, like you said, mostbusinesses are pro for profit.
(22:25):
So they think in dollar signs andusually pretty much dollar signs only.
Uh, so if we can't relate ouranalysis and our work that we've
done into dollar signs, and itdoesn't have to be dollar signs.
It can be time saved.
It could be lives saved.
It could be.
You know, people promoted, I don'tknow, whatever, whatever the,
the key units, yeah, more users.
(22:46):
That's, that's another good one.
Whatever your team is focusedon, you have to figure out how
to get your analysis there.
Otherwise you're not reallyhelping the team out one and two.
Like you said, your, your careergrowth is going to struggle because.
Especially these bigger companies,like your promotions are kind of tied
to the work you've done for the impactyou've had for the business, basically.
Daliana Lui (23:07):
Yeah.
Avery (23:07):
Yeah.
Okay.
I, I agree with you there.
I think that that makes a lot of sense.
Another thing I think you, youmentioned in a LinkedIn post is like.
Maybe you are trying to do that, right?
But you're like, you're kind of strugglingto, to like advocate for yourself.
You're kind of struggling talk toyour, to make, to make your work clear.
Do you have any advice on likehow to like make your, your work
(23:28):
more known like in the company?
Daliana Lui (23:30):
Um, yeah, so.
Uh, you meant, uh, making, havingmore visibility in a company?
Avery (23:37):
Yes.
Daliana Lui (23:38):
Yeah.
If you already work on a highimpact project, you probably will
work on sometime directors or VP.
So I don't think you need to be kind ofquote unquote famous in your company.
Of course it helps, right?
If you did deliver a high impactproject and you give a talk.
You have more visibility.
Maybe there are other people come to,um, invite you to for collaboration.
(24:03):
For example, when I published a blogpost on the soccer project, which is
talking about there are other teamsreaching out to me, asking questions,
looking for collaborations, but a lot oftimes you only need to be visible to a.
In their circle of people, for example,the people who actually decide the road
(24:23):
map of the team or the person who might beon the committee of your promotion review,
I think a great way to do this is to.
See if you can build a relationshipwith them to collaborate with them.
And the first step is, uh, if you,again, don't know how to build a
relationship with, you can go from aperspective to just learn from them,
(24:47):
to get feedback and show them what'ssomething you have been working on.
Kind of similar to, we talk aboutgetting stakeholder feedback.
Um, if you want to bring more awarenessfor your project, for example, you're
building a new tool that will improve.
You aim to improve your team'sproductivity, maybe talk to the key
users, potential users of this tool orsome other stakeholders and show them
(25:10):
a quick demo, um, ask them what's theirpain point and get, get a user feedback.
So when someone is.
Involved when they, uh, give youideas and you implement them, they
feel they're part of the project.
So later on, when they have some similarproject, they're aware, Oh, there is
someone I can talk to on that team.
They're expert in this.
(25:31):
So in a company, you don'thave to be an expert.
You don't need to work on one project for.
10 years and have a PhDin it to become expert.
Sometimes if you deliver project end toend, you, you know, a lot of the domain
knowledge and the business contact,you are an expert, let people, uh, by
collecting feedback, um, talk to peopleone on one, um, sometimes help them.
(25:55):
People know that you arethe expert on this domain.
And when you finish the project,share your work through an internal
blog post, or you can schedule alunch and learn session, et cetera.
And, uh, I know we allhave our own priorities.
We're busy, but sometimes also need to,you can set aside some time to host.
(26:16):
Office hours or, um, Q and asessions, be generous with your time.
Sometimes also goes a long way.
Avery (26:23):
Very cool.
And I think, I think that isawesome advice on, on increasing
availability, uh, sharing your work.
It's such a, seems, seems like youshouldn't have to do that because
you're at work and it's like, whydo I have to share this with anyone?
Uh, but it can be such a big,uh, impact to your career.
And, and others as well.
Uh, well, Dalyana, this is theData Career Podcast, and obviously
(26:44):
you've shared a lot of good thingsabout growing your data career.
Uh, I want to ask you if you had togive someone who, you know, is listening
to this episode, any sort of advice onadvancing their career to the next level.
What would you give them?
Daliana Lui (26:55):
I have so many devices, very hard to come down to one.
Yeah, I would say.
There is, of course, it's importantto understand how to create more
impact for your company, uh,how to advocate for yourself.
We are, um, in this kind of system,there's promotion, there's annual review.
(27:16):
It's important to knowhow to play that game.
Uh, but at the same time, it'salso important to look inward.
To know what do you enjoy, what is yourgoal, uh, what's your life goal beyond
your, the, the next level of the promotionor the raise, I think is helpful for
you to play the long game, um, whenyou know yourself better, so maybe.
(27:41):
Uh, every quarter or every year said,uh, we're at the end of the year,
maybe during the holiday season said,uh, one afternoon, just write down how
do envision your life will look like.
And then think about how couldyour career, your family, your
friends play a part of it.
So at the end of the day, the careeris only one aspect of our life.
Avery (28:01):
I think that's important to remember because it it's really easy to
get lost in, uh, all in it all because.
It's like, why do we work?
We work to live.
And sometimes it feelslike we live to work.
Um, so I think that is sage advice.
Uh, Dalyana, thank youso much for coming on.
We'll have all of Dalyana's, uh,links in the show notes down below.
She's been working onsomething really cool as well.
(28:23):
Dalyana, you want to talk aboutwhat you've been doing recently?
Daliana Lui (28:25):
Yeah, so I'm working on, uh, more career coaching.
So one course I recentlylaunched is called the data
science career accelerator.
So we talked about how to, uh, improveyour stakeholder management skills.
How to be a great communicator.
So all the soft skills we just talkabout and how to build a relationship
(28:47):
with our manager, how to create moreimpact and get a promotion you deserve.
So basically we teach you all therequired soft skills, leadership
skills, communication skillsthat school didn't teach you.
And this course requires youto be a data scientist for,
you know, at least one year.
(29:08):
Um, and, uh, a lot of, uh, the seniordata scientists take this course too.
They want to learn how tocontinue to expand their scope.
So, um, I will share thelink with, um, Avery.
Avery (29:19):
Yep.
We'll have the link in theshow notes, uh, down below.
We'll also have linksto your social as well.
So make sure you'refollowing Dalyana already.
Dalyana, thanks so muchfor being on the show.
Daliana Lui (29:29):
Thanks Avery.
I think a lot of times we think it's important to
constantly grow your technical skills,but that only get you somewhere.
So basically, if you imagine the careertrajectory from junior data scientist
to senior data scientist and later staffand principal scientist, you'll see the
requirement for technical skills slowly.
(00:20):
The increase is there.
Not that high.
And however, it requires morecommunication skills, leadership
skills, influencing skills, higherlevel you want to, um, become.
Avery (00:32):
So Dalyana, you have almost 300, 000 followers on LinkedIn.
You're a LinkedIn top voice.
You're the host of thedata scientist show.
Uh, you've worked as a data scientist,uh, at Amazon, and now you're kind of
doing your own thing, teaching otherpeople how to be data scientists.
Thank you so much for comingon the podcast and, uh, I'm
so excited to have you here.
Daliana Lui (00:51):
Yeah, thanks for having me Avery.
It's been a long time coming.
Avery (00:54):
Yeah, I've been on your show and now you're coming on, on mine, but
I'm really excited for, for me to getto know your story better and also for
our audience to know your story more.
And also just know more aboutlike what it actually takes to
be a data scientist, you know,specifically at a company like Amazon.
So for those who maybe haven'tfollowed you in the past, can you
just give like a quick overviewof what your career has been like?
Daliana Lui (01:17):
Yeah, so I started studied applied math in college when
I lived in China, and then I felt itwas too much theories, and I wanted
to learn something more practical.
So that's when I startedto get into statistics.
Got my master in, um, University ofIrvine, University of California.
(01:40):
Uh, and then I got my job as a businessintelligence data, data analyst.
So at that time the word data scientistwasn't invented, but I was basically
doing, uh, data science generalist work.
Uh, I'm doing.
Analysis for the marketingteam, build time series model.
And then I, uh, got into Amazon.
(02:03):
I moved from LA to Seattle.
Uh, my title was again, businessintelligence engineer slash statistician.
I think that's a.
Basically perfect kind ofrole for a data scientist.
Uh, and later I work on experimentation,AB testing, product analytics.
(02:24):
So that's the first few years in Amazon.
And then I got into machine learningand deep learning and moved to,
um, Amazon web services and alsogrow to a senior data scientist.
Avery (02:36):
Very cool.
And I think it's so interesting that youstarted your career as a data analyst
or like with that data analyst title.
Do you recommend that for others?
Like starting as a data analyst or wasthat role already kind of a data scientist
role, but just had the title of data.
Daliana Lui (02:51):
I think today, if you look at what people do under a data scientist
or data analyst role, it's so different.
For example, at Facebook, thereare product data scientists.
Don't do much machinelearning and modeling.
And they write a lot of SQL and theyprobably also use Python and in some
(03:12):
other companies, there might be adata analyst also doing some modeling.
So I would say it really depends onthe company and what a role specifies,
but in general, data scientists do.
Use Python more, do a little bit moreautomation compared to data analysts.
And some data analysts, theywork more as a business analyst.
(03:35):
They work closely, veryclosely with, um, stakeholders.
I don't think there's a good or bad tostart your career where it really depends
on what you're interested in, what kindof job, uh, market look like, and, uh,
uh, Regardless of where you get started,you can grow and become either a manager
(03:56):
or a principal data scientist and analystin your, uh, in your career track.
Avery (04:01):
I agree that there's probably not a right or a wrong necessarily,
but one thing, one thing that is reallyinteresting is just that those titles
kind of being all over the place.
And I think that's true today.
I mean, I think it'sprobably more true back then.
Then, but even today, like I see someof the strangest titles, like I've
seen a data science analyst before,uh, or data analytic scientists.
Yeah.
And I'm like, I don't know exactlywhat, what those roles are.
(04:23):
So if I had to ask you,what is a data scientist?
What would you say the definitionof a data scientist is?
Daliana Lui (04:28):
Yeah.
I think the data scientist is someonewho uses data and some kind of framework.
Could be experimentation, could be,uh, machine learning, or it could
be some kind of statistics analysisto help their usually business, uh,
stakeholder make better decisions.
(04:49):
Um, and this decision could be onedecision could be you automate a million
decisions, making it into a machinelearning model and eventually have
some sort of business impact, meaningit help your company make more money.
Uh, save more time, save money, et cetera.
Getting more customers.
Avery (05:06):
I like that definition.
Do you, do you think that like, doyou think that there's a difference?
Cause like a data scientist is kindof what you just explained, right?
And there's this whole fieldof study called data science.
Do you think data scientists arethe only ones that do data science
or like, where do you see datascience versus data analytics?
Daliana Lui (05:24):
Yeah, I think now everybody does data science, not just data
analysts, product managers, they haveto know some data science, data science.
They might not be the onethat always writes SQL, but
they need to understand it.
And I've also seen a lot of automatedanalytics or machine learning tools.
(05:44):
Maybe a product manager in thefuture can easily use those
tools to create some analysis.
Engineers.
They need to know data science.
In fact, a lot of AI engineers thesedays, they basically came from a
software engineering backgroundand then they learned machine
learning statistics on the go.
And of course, we'lltalk about the overlap.
I think the biggest difference isin general, I would say, See the
(06:08):
data scientists, the people witha data scientist title, um, some
of them work on machine learning.
Some of them don't, but the ones whowork on machine learning, deep learning
have more engineering element in it.
See them more often ina data scientist title.
And for a data analyst.
I think the data analysts today also doa lot of automation, but it probably lies
(06:29):
in, uh, some of them might do some dataengineering work or creating a dashboard,
new automated dashboard, but it doesn'tmean their work is easily automated.
Need to communicate a lot withtheir stakeholders to find out
what is the most important thing.
What's the story you need totell from their, uh, dashboard.
Uh, so they probably use moreSQL and some data analysts.
(06:53):
I know they use a lot ofExcel as well because their
stakeholders are not technical.
Avery (06:58):
I think that's like a good definition because really at the day.
There is so much overlap.
Um, and it really, like yousaid, depends on the company.
So it, it's, it's quite difficultto, to actually draw a line on, uh,
let's talk about some of the workthat, uh, you've done in your career.
So as a data scientist, you, you mentionedthe term machine learning, which, which I
think is a term that a lot of people hear,but maybe don't know the definition of.
(07:21):
For, for me, it's basically justusing some sort of, of math to
accomplish some business problem.
The biggest one probably is predicting,uh, what's going to happen in the
future, but there's obviously otherthings like, you know, separating
things into groups and stuff like that.
Would you say that's kind of a fairdefinition of machine learning?
Daliana Lui (07:40):
Yeah, I think so.
It's basically learning machine learningis basically learning patterns from data.
If you think about a pattern oflet's just say you drink coffee on
Monday, Wednesday, Friday, but don'tdrink coffee on the rest of the day.
So if I have enough data, if youfollow those patterns, say 90
(08:01):
90 percent of the time, I'm ableto use a model to learn that.
So I think that's the.
Simplest machine learning probablyjust have like one parameters,
which is the day of the week.
Uh, and today, when we think aboutmachine learning, it's more complicated.
We're probably some models.
If you think about the open AI hadGPT probably have, uh, I don't know,
(08:22):
millions, billions of those parameters.
Avery (08:26):
Yeah, that that's pretty complicated stuff, but you've also
worked on some pretty complicated stuff.
I would imagine at Amazon, one ofthe things that I saw that you, you,
you'd kind of co published with Amazonwas essentially a soccer project,
which I played soccer growing up.
So I was a big fan.
Yeah.
And I think a lot of people listeningreally, really enjoy sports.
(08:47):
Maybe they've seen the movie Moneyballor read the book Moneyball kind
of highlighting the Oakland A'sand how they used analytics to,
to, you know, win a championship.
Uh, can you talk a little bit aboutwhat you kind of did at Amazon
with, with this soccer project?
Daliana Lui (09:00):
I was at Amazon Web Services and, uh, our team at that
time was called ML Solutions Lab.
So basically we're a group of consultants.
We help AWS customers, um, implement amachine learning, deep learning solution.
So this customer came to us.
They are a sports betting company.
They have a lot of soccer game dataand they want to see whether they
(09:23):
can predict whether there will be asoccer goal in the next few seconds.
for joining us.
And so this is the first computervision project I worked on.
And it's a very complicated projectbecause we need to analyze the videos.
Uh, we basically, um, used a few differentframeworks to chop the data, um, into
(09:46):
kind of five second, five seconds, sevensecond clips, and then we have to manually
Manually label the data into whether thismoment is a goal, whether it's not a goal.
And if you watch soccer, youknow, sometimes a very intense,
uh, how do you call it?
Uh, attack.
It looks very similar to a goal.
(10:07):
So we also need to label that to traina model, to learn this is attack.
This is not a goal.
So a fun story is becausethe data came to us.
We're not labeled.
So me and my coworker spent two days.
Just looking at those clips to labelwhether this is goal or it's not a goal.
So over the two days, I think I probablywatched hundreds of soccer goals.
(10:31):
I don't want to watch soccerfor the next couple of years.
So that's the unexpectedpart of data science.
Sometimes you need to do a lot of thosetype of data quality check, labeling.
But you have to do those type of thingsbecause we, we label it in a very specific
way that we know how to train a model.
Eventually, uh, we used, uh,we experiment that on a few
(10:54):
different, uh, video analysis,uh, modeling called, uh, um, I3D.
So basically it's an inflated 2D.
Using, uh, inflated 2D modeling toanalyze, uh, the data and the way,
how to simplify the business problem.
Because like I mentioned, aftertech, there could be a goal.
(11:14):
It could be not a goal,maybe ended in like a corner.
For example, we'll just simplifythat into a binary problem.
That's also, um, important way to tacklea very ambiguous, complicated problem.
Sometime you might not, youmight need to reduce the scope.
Uh, for example, this, in this case,we reduce the problem space from a
(11:36):
multi class classification probleminto a binary classification problem.
And then we train a model.
When we came out with a classifier, uh,with a classifier, we Use a classifier
to run through the entire game.
Every five seconds, we run through thatclassifier and then see whether there
(11:57):
will be a goal in the next few seconds.
We also created a very fun demo.
Basically, in real time, you can see aUh, likelihood score of whether there
will be a goal in the next few seconds.
It can make the viewingexperience more exciting.
Avery (12:13):
Yeah.
I saw, I saw the demo actually, andmaybe I'll, I'll insert a little
recording because it was pretty cool.
Um, but what, what a cool project.
Uh, and I think.
Uh, there's so manydifferent, different things.
I think that listeners can, can learnfrom that one companies like Amazon.
And honestly, a lot of companies are actas consulting companies a lot of the time.
Uh, and so what, like a gamblingcompany in this case, or any other
(12:35):
sort of manufacturing company,or I don't know, whatever.
Company that exists a lot of the timesthey like kind of outsource their
analytics and data structure stuff tosmarter companies like, like Amazon.
And I think that's, that's good to know.
And I think that's a cool role to sitin is basically you get to do analytics
for, for multiple, multiple companies.
I think that's really cool.
And then the other thing I love thatyou said was, you know, I didn't seem
(12:56):
like you were too much of a soccer fannecessarily, and you to become one.
Uh, and that's sometimes what youhave to do is like, you maybe don't
have the domain experience, but.
You can kind of need the domainexperience when you're building machine
learning models a lot of the time.
Daliana Lui (13:10):
Yeah, exactly.
And I also worked on a football, Americanfootball project when I was on the team.
I knew, I mean, I, I know a littlebit of soccer, of course, but I knew
nothing about American football and Ihave to buy a book to read how football
work, uh, to, to, you know, to ourpoint, to understand the context.
Avery (13:30):
It's crazy because yeah, it's just, there's the cool thing about
analytics and data science and machinelearning is it's really industry agnostic,
which really means that you can takethe principles, the machine learning,
um, models and the machine learningalgorithms and apply them to really
so many different business problems.
And so you could probably spend yourwhole life just learning about different
industries and how to apply just one modelto those, those different industries.
(13:53):
Uh, which I, which I think is fascinatingand one thing I want to give you
credit for in your LinkedIn content.
A lot of the times you're,you're obviously very technical.
Like you use a lot of very fun buzzwords,uh, when you're kind of explaining that
and you've obviously worked for Amazon.
So you're obviously very technical, butone thing I really appreciate about your
LinkedIn posts is, you know, sometimesthey're technical, but other times they're
like, Hey, you as a technical personactually kind of get more done when
(14:17):
you focus on your non technical skills.
Has that been true for you in your career?
Daliana Lui (14:22):
Yeah, absolutely.
Uh, I think a lot of times wethink it's important to constantly
grow your technical skills, butthat only get you, um, somewhere.
And after that, uh, I wishI could show you a plot.
So basically if you imagine the careertrajectory from junior data scientists
to senior data scientists, and laterstaff and principal scientists, you'll
(14:45):
see the requirement for technical skills.
Slowly, the increases.
Not that high, and however you require,it requires more communication skills,
leadership skills, influencing skills,higher level you want to become.
And I think once we get into thereality, there's no homework anymore,
(15:07):
and there's no, uh, perfect data.
And a lot of times the stakeholders arenot even clear about what they want.
And so it is essential to know.
You know, from the beginning of theproject, how to ask the right questions,
how to work with the right people,how to find a project that actually
have the high impact that can get youa promotion and later on, how do you
(15:28):
influence the right stakeholders toget your solution in the right place?
Avery (15:33):
It's a, it's a crazy concept because I think we, we like to think
as technical people, the, the moretechnical you are, the more you'll get
paid, the more desirable you'll be, themore influence you'll have at a company.
And, and to be honest, it's just not true.
Even if you're not junior levels,even when you're trying to get hired.
It's not like the smartest person orthe best person at SQL lands the job.
(15:55):
There's often these soft skills, thesepeople skills, these communication
skills, uh, that come into play and,and really kind of make the difference
between maybe a good data analyst, agood data scientist, and a great one.
Um, one of the ones that you posted aboutrecently, and I think you kind of just.
Hinted at it just barely.
And your answer was sometimesthese stakeholders don't have
a clue, uh, of what you want.
And so one of the things youposted recently was like, one thing
(16:17):
that can make you a great datascientist is getting feedback early.
Can you expound on that?
Daliana Lui (16:22):
Uh, when I started, uh, in Amazon, I wanted to show
my manager where my stakeholders,my work only one is perfect.
Otherwise I would feel embarrassed.
But reality is.
Sometimes you think you understandtheir request, but you don't.
Or during the time when you're workingon a project, their preference,
(16:43):
their priority have changed.
So it's important to constantlyalign with your stakeholders to make
sure you understand their needs.
And also, there's only It's verylimiting what you can communicate
three words, especially you'reworking on a data science project,
whether you need to turn that into adashboard or machine learning model.
(17:04):
So you have to show them your demo.
Um, I, in my career growthcourse, I always talk about
show them a ugly demo first.
Even if it's just in your, um, Jupyternotebook or in your, you know, SQL,
uh, you know, editor, show them to letthem know what's the, uh, what does
(17:24):
the MVP look like, it's even betterif you can create a very small UI
so they can play with, they can getexcited for, and when they see what.
It might look like it gave them more idea.
So it's not a bad thing when they tellyou, Hey, this is not what I want.
If you're only 20 percent of theproject, but it will be a huge problem.
(17:46):
If you're already at 80 percentof projects, actually you want to,
uh, have those small tweaks and askthem, Hey, is this what you want?
Or I have a few other ideas thatI think that might help you.
This is my proposals.
What do you think?
So have those conversationsearly can save you a lot of time.
When you're towardsthe end of the project,
Avery (18:07):
I'm sure, I'm sure you've seen this now as you've grown in your career.
And I've seen it as I've grown in mycareer to the point now where I, you
know, I code a little bit, but a lotof what I do is, is directing other
people to code and stuff like that.
And I realized that, you know, now I'mthe stakeholder and I've become the, a
bad stakeholder where I don't even knowwhat I want half the time, what I'm
asking, or when I do know what I want, Ikind of stink at explaining what I want.
(18:31):
Um, and so when people.
You know, who, who are workingunder me, are able to come back
with something quickly and be like,Hey, is this what you're asking?
Uh, especially like in a meeting or ina demo, like a loom video, uh, because
I like, like what you said, you,you can only say so much with words.
It's almost like, likeinternet speed, right?
That's basically like how fast informationcan transfer words is like, I don't know.
(18:52):
15 megabytes per second, butlike an in person meeting,
we're talking like gig speed.
Um, there's just so muchmore communication, which is,
which is, which is awesome.
And that ultimately leads towhat, what's called like adoption
and people using your analysis.
Um, and that's another thing that you'vementioned, uh, on LinkedIn that like.
You can't really do datascience just for, for funsies.
(19:14):
You have to get it adopted.
Can you talk a little bit more about that?
Daliana Lui (19:17):
I think there was a data point a few years ago, probably over 80
percent of machine learning models fail.
I think part of it is naturalbecause there's a research or
discovery nature in data science.
Not everything has tobe put in production.
But a lot of times if Whatyou have done become useless.
(19:39):
Then from the company perspective,they wasted their time.
You don't have direct impact.
And from a personal growth perspective,if you don't have the impact, it's
hard to define your contributionto the team, to our growth.
And how do you advocate?
Yourself for that promotion, whenyou build something, a lot of data
(20:01):
scientists and also engineers, they wantto just build something that they think
is important or they think is cool.
They just learn some modelfrom a Coursera course.
They want to implement that.
I think that's a great way to learn.
By doing, but when it comes to, um, doingwork for a, most of the time for profit
company, you need to think about is whatI'm working on aligned with my team goal.
(20:26):
I'm not helping my stakeholderor, um, this is the five goals.
My manager tried to achieve.
I'm not helping my manager.
I might be a team player.
It's better if you can alignyour passion to the impact.
And sometimes the passion andimpact might be a separate thing.
There is one thing maybe you can exerciseyour passion for, for learning on your
(20:48):
own time or take 20 percent of, you know,your, your work time, but make sure the
80 percent of your time, you're actuallysolving the useful business problem.
And a lot of time, itcould be a little bit, um.
Boring and repetitive.
Um, I think that's also an opportunityfor you to create more impact, to
see how can you, um, automate this?
(21:10):
How can you also, sometimes you need tomotivate yourself that again, aligning
with the stakeholders, with the customer'spain point, stakeholder's request.
Sometimes if you see how does thatimplement it, how it solve even
just one person's problem, it can.
Also make you feel more motivatedto work on projects like that.
Avery (21:30):
It's hard because in, in data, especially like in school, right?
Let's just take like a normal, you know,college, maybe like a master's degree
or maybe even an undergrad degree.
Your master's was in what again?
What did you say your master's was in?
Daliana Lui (21:42):
In statistics.
Avery (21:43):
Okay.
And yeah, and my master's was in,was in data analytics technically.
Right.
Um, but like, I would imagine itwas the same in your master's, but
my master's was very theoretical.
Um, and it was all about like, like, forinstance, you, you might be interested in
statistics of, of like getting a P valueless than, you know, zero point or 0.
05.
(22:04):
And you might be interested in like, okay.
Like, can we make the P value lower?
I know we can't really make P valueslower, but like you might be interested
in really low P value or, or inlike my masters, it might be like,
Hey, we're like 79 percent accurate,can I get to 81 percent accurate?
So we're thinking in likeP values and percentages.
But really, like you said, mostbusinesses are pro for profit.
(22:25):
So they think in dollar signs andusually pretty much dollar signs only.
Uh, so if we can't relate ouranalysis and our work that we've
done into dollar signs, and itdoesn't have to be dollar signs.
It can be time saved.
It could be lives saved.
It could be.
You know, people promoted, I don'tknow, whatever, whatever the,
the key units, yeah, more users.
(22:46):
That's, that's another good one.
Whatever your team is focusedon, you have to figure out how
to get your analysis there.
Otherwise you're not reallyhelping the team out one and two.
Like you said, your, your careergrowth is going to struggle because.
Especially these bigger companies,like your promotions are kind of tied
to the work you've done for the impactyou've had for the business, basically.
Daliana Lui (23:07):
Yeah.
Avery (23:07):
Yeah.
Okay.
I, I agree with you there.
I think that that makes a lot of sense.
Another thing I think you, youmentioned in a LinkedIn post is like.
Maybe you are trying to do that, right?
But you're like, you're kind of strugglingto, to like advocate for yourself.
You're kind of struggling talk toyour, to make, to make your work clear.
Do you have any advice on likehow to like make your, your work
(23:28):
more known like in the company?
Daliana Lui (23:30):
Um, yeah, so.
Uh, you meant, uh, making, havingmore visibility in a company?
Avery (23:37):
Yes.
Daliana Lui (23:38):
Yeah.
If you already work on a highimpact project, you probably will
work on sometime directors or VP.
So I don't think you need to be kind ofquote unquote famous in your company.
Of course it helps, right?
If you did deliver a high impactproject and you give a talk.
You have more visibility.
Maybe there are other people come to,um, invite you to for collaboration.
(24:03):
For example, when I published a blogpost on the soccer project, which is
talking about there are other teamsreaching out to me, asking questions,
looking for collaborations, but a lot oftimes you only need to be visible to a.
In their circle of people, for example,the people who actually decide the road
(24:23):
map of the team or the person who might beon the committee of your promotion review,
I think a great way to do this is to.
See if you can build a relationshipwith them to collaborate with them.
And the first step is, uh, if you,again, don't know how to build a
relationship with, you can go from aperspective to just learn from them,
(24:47):
to get feedback and show them what'ssomething you have been working on.
Kind of similar to, we talk aboutgetting stakeholder feedback.
Um, if you want to bring more awarenessfor your project, for example, you're
building a new tool that will improve.
You aim to improve your team'sproductivity, maybe talk to the key
users, potential users of this tool orsome other stakeholders and show them
(25:10):
a quick demo, um, ask them what's theirpain point and get, get a user feedback.
So when someone is.
Involved when they, uh, give youideas and you implement them, they
feel they're part of the project.
So later on, when they have some similarproject, they're aware, Oh, there is
someone I can talk to on that team.
They're expert in this.
(25:31):
So in a company, you don'thave to be an expert.
You don't need to work on one project for.
10 years and have a PhDin it to become expert.
Sometimes if you deliver project end toend, you, you know, a lot of the domain
knowledge and the business contact,you are an expert, let people, uh, by
collecting feedback, um, talk to peopleone on one, um, sometimes help them.
(25:55):
People know that you arethe expert on this domain.
And when you finish the project,share your work through an internal
blog post, or you can schedule alunch and learn session, et cetera.
And, uh, I know we allhave our own priorities.
We're busy, but sometimes also need to,you can set aside some time to host.
(26:16):
Office hours or, um, Q and asessions, be generous with your time.
Sometimes also goes a long way.
Avery (26:23):
Very cool.
And I think, I think that isawesome advice on, on increasing
availability, uh, sharing your work.
It's such a, seems, seems like youshouldn't have to do that because
you're at work and it's like, whydo I have to share this with anyone?
Uh, but it can be such a big,uh, impact to your career.
And, and others as well.
Uh, well, Dalyana, this is theData Career Podcast, and obviously
(26:44):
you've shared a lot of good thingsabout growing your data career.
Uh, I want to ask you if you had togive someone who, you know, is listening
to this episode, any sort of advice onadvancing their career to the next level.
What would you give them?
Daliana Lui (26:55):
I have so many devices, very hard to come down to one.
Yeah, I would say.
There is, of course, it's importantto understand how to create more
impact for your company, uh,how to advocate for yourself.
We are, um, in this kind of system,there's promotion, there's annual review.
(27:16):
It's important to knowhow to play that game.
Uh, but at the same time, it'salso important to look inward.
To know what do you enjoy, what is yourgoal, uh, what's your life goal beyond
your, the, the next level of the promotionor the raise, I think is helpful for
you to play the long game, um, whenyou know yourself better, so maybe.
(27:41):
Uh, every quarter or every year said,uh, we're at the end of the year,
maybe during the holiday season said,uh, one afternoon, just write down how
do envision your life will look like.
And then think about how couldyour career, your family, your
friends play a part of it.
So at the end of the day, the careeris only one aspect of our life.
Avery (28:01):
I think that's important to remember because it it's really easy to
get lost in, uh, all in it all because.
It's like, why do we work?
We work to live.
And sometimes it feelslike we live to work.
Um, so I think that is sage advice.
Uh, Dalyana, thank youso much for coming on.
We'll have all of Dalyana's, uh,links in the show notes down below.
She's been working onsomething really cool as well.
(28:23):
Dalyana, you want to talk aboutwhat you've been doing recently?
Daliana Lui (28:25):
Yeah, so I'm working on, uh, more career coaching.
So one course I recentlylaunched is called the data
science career accelerator.
So we talked about how to, uh, improveyour stakeholder management skills.
How to be a great communicator.
So all the soft skills we just talkabout and how to build a relationship
(28:47):
with our manager, how to create moreimpact and get a promotion you deserve.
So basically we teach you all therequired soft skills, leadership
skills, communication skillsthat school didn't teach you.
And this course requires youto be a data scientist for,
you know, at least one year.
(29:08):
Um, and, uh, a lot of, uh, the seniordata scientists take this course too.
They want to learn how tocontinue to expand their scope.
So, um, I will share thelink with, um, Avery.
Avery (29:19):
Yep.
We'll have the link in theshow notes, uh, down below.
We'll also have linksto your social as well.
So make sure you'refollowing Dalyana already.
Dalyana, thanks so muchfor being on the show.
Daliana Lui (29:29):
Thanks Avery.
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