147: The Surprising TRUTH About Data Science Careers (ex-Amazon data scientist Daliana Liu)

Daliana Lui: 00:00:00

I think a lot of times we think it's important to

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constantly grow your technical skills,

but that only get you somewhere.

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So basically, if you imagine the career

trajectory from junior data scientist

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to senior data scientist and later staff

and principal scientist, you'll see the

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requirement for technical skills slowly.

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The increase is there.

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Not that high.

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And however, it requires more

communication skills, leadership

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skills, influencing skills, higher

level you want to, um, become.

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Avery: So Dalyana, you have almost

300, 000 followers on LinkedIn.

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You're a LinkedIn top voice.

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You're the host of the

data scientist show.

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Uh, you've worked as a data scientist,

uh, at Amazon, and now you're kind of

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doing your own thing, teaching other

people how to be data scientists.

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Thank you so much for coming

on the podcast and, uh, I'm

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so excited to have you here.

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Daliana Lui: Yeah, thanks

for having me Avery.

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It's been a long time coming.

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Avery: Yeah, I've been on your show

and now you're coming on, on mine, but

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I'm really excited for, for me to get

to know your story better and also for

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our audience to know your story more.

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And also just know more about

like what it actually takes to

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be a data scientist, you know,

specifically at a company like Amazon.

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So for those who maybe haven't

followed you in the past, can you

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just give like a quick overview

of what your career has been like?

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Daliana Lui: Yeah, so I started

studied applied math in college when

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I lived in China, and then I felt it

was too much theories, and I wanted

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to learn something more practical.

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So that's when I started

to get into statistics.

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Got my master in, um, University of

Irvine, University of California.

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Uh, and then I got my job as a business

intelligence data, data analyst.

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So at that time the word data scientist

wasn't invented, but I was basically

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doing, uh, data science generalist work.

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Uh, I'm doing.

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Analysis for the marketing

team, build time series model.

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And then I, uh, got into Amazon.

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I moved from LA to Seattle.

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Uh, my title was again, business

intelligence engineer slash statistician.

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I think that's a.

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Basically perfect kind of

role for a data scientist.

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Uh, and later I work on experimentation,

AB testing, product analytics.

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So that's the first few years in Amazon.

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And then I got into machine learning

and deep learning and moved to,

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um, Amazon web services and also

grow to a senior data scientist.

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Avery: Very cool.

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And I think it's so interesting that you

started your career as a data analyst

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or like with that data analyst title.

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Do you recommend that for others?

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Like starting as a data analyst or was

that role already kind of a data scientist

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role, but just had the title of data.

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Daliana Lui: I think today, if you look

at what people do under a data scientist

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or data analyst role, it's so different.

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For example, at Facebook, there

are product data scientists.

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Don't do much machine

learning and modeling.

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And they write a lot of SQL and they

probably also use Python and in some

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other companies, there might be a

data analyst also doing some modeling.

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So I would say it really depends on

the company and what a role specifies,

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but in general, data scientists do.

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Use Python more, do a little bit more

automation compared to data analysts.

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And some data analysts, they

work more as a business analyst.

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They work closely, very

closely with, um, stakeholders.

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I don't think there's a good or bad to

start your career where it really depends

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on what you're interested in, what kind

of job, uh, market look like, and, uh,

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uh, Regardless of where you get started,

you can grow and become either a manager

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or a principal data scientist and analyst

in your, uh, in your career track.

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Avery: I agree that there's probably

not a right or a wrong necessarily,

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but one thing, one thing that is really

interesting is just that those titles

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kind of being all over the place.

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And I think that's true today.

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I mean, I think it's

probably more true back then.

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Then, but even today, like I see some

of the strangest titles, like I've

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seen a data science analyst before,

uh, or data analytic scientists.

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Yeah.

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And I'm like, I don't know exactly

what, what those roles are.

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So if I had to ask you,

what is a data scientist?

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What would you say the definition

of a data scientist is?

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Daliana Lui: Yeah.

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I think the data scientist is someone

who uses data and some kind of framework.

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Could be experimentation, could be,

uh, machine learning, or it could

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be some kind of statistics analysis

to help their usually business, uh,

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stakeholder make better decisions.

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Um, and this decision could be one

decision could be you automate a million

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decisions, making it into a machine

learning model and eventually have

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some sort of business impact, meaning

it help your company make more money.

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Uh, save more time, save money, et cetera.

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Getting more customers.

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Avery: I like that definition.

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Do you, do you think that like, do

you think that there's a difference?

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Cause like a data scientist is kind

of what you just explained, right?

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And there's this whole field

of study called data science.

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Do you think data scientists are

the only ones that do data science

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or like, where do you see data

science versus data analytics?

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Daliana Lui: Yeah, I think now everybody

does data science, not just data

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analysts, product managers, they have

to know some data science, data science.

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They might not be the one

that always writes SQL, but

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they need to understand it.

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And I've also seen a lot of automated

analytics or machine learning tools.

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Maybe a product manager in the

future can easily use those

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tools to create some analysis.

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Engineers.

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They need to know data science.

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In fact, a lot of AI engineers these

days, they basically came from a

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software engineering background

and then they learned machine

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learning statistics on the go.

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And of course, we'll

talk about the overlap.

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I think the biggest difference is

in general, I would say, See the

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data scientists, the people with

a data scientist title, um, some

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of them work on machine learning.

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Some of them don't, but the ones who

work on machine learning, deep learning

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have more engineering element in it.

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See them more often in

a data scientist title.

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And for a data analyst.

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I think the data analysts today also do

a lot of automation, but it probably lies

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in, uh, some of them might do some data

engineering work or creating a dashboard,

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new automated dashboard, but it doesn't

mean their work is easily automated.

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Need to communicate a lot with

their stakeholders to find out

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what is the most important thing.

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What's the story you need to

tell from their, uh, dashboard.

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Uh, so they probably use more

SQL and some data analysts.

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I know they use a lot of

Excel as well because their

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stakeholders are not technical.

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Avery: I think that's like a good

definition because really at the day.

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There is so much overlap.

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Um, and it really, like you

said, depends on the company.

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So it, it's, it's quite difficult

to, to actually draw a line on, uh,

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let's talk about some of the work

that, uh, you've done in your career.

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So as a data scientist, you, you mentioned

the term machine learning, which, which I

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think is a term that a lot of people hear,

but maybe don't know the definition of.

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For, for me, it's basically just

using some sort of, of math to

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accomplish some business problem.

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The biggest one probably is predicting,

uh, what's going to happen in the

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future, but there's obviously other

things like, you know, separating

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things into groups and stuff like that.

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Would you say that's kind of a fair

definition of machine learning?

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Daliana Lui: Yeah, I think so.

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It's basically learning machine learning

is basically learning patterns from data.

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If you think about a pattern of

let's just say you drink coffee on

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Monday, Wednesday, Friday, but don't

drink coffee on the rest of the day.

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So if I have enough data, if you

follow those patterns, say 90

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90 percent of the time, I'm able

to use a model to learn that.

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So I think that's the.

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Simplest machine learning probably

just have like one parameters,

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which is the day of the week.

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Uh, and today, when we think about

machine learning, it's more complicated.

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We're probably some models.

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If you think about the open AI had

GPT probably have, uh, I don't know,

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millions, billions of those parameters.

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Avery: Yeah, that that's pretty

complicated stuff, but you've also

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worked on some pretty complicated stuff.

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I would imagine at Amazon, one of

the things that I saw that you, you,

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you'd kind of co published with Amazon

was essentially a soccer project,

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which I played soccer growing up.

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So I was a big fan.

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Yeah.

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And I think a lot of people listening

really, really enjoy sports.

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Maybe they've seen the movie Moneyball

or read the book Moneyball kind

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of highlighting the Oakland A's

and how they used analytics to,

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to, you know, win a championship.

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Uh, can you talk a little bit about

what you kind of did at Amazon

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with, with this soccer project?

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Daliana Lui: I was at Amazon Web

Services and, uh, our team at that

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time was called ML Solutions Lab.

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So basically we're a group of consultants.

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We help AWS customers, um, implement a

machine learning, deep learning solution.

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So this customer came to us.

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They are a sports betting company.

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They have a lot of soccer game data

and they want to see whether they

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can predict whether there will be a

soccer goal in the next few seconds.

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for joining us.

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And so this is the first computer

vision project I worked on.

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And it's a very complicated project

because we need to analyze the videos.

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Uh, we basically, um, used a few different

frameworks to chop the data, um, into

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kind of five second, five seconds, seven

second clips, and then we have to manually

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Manually label the data into whether this

moment is a goal, whether it's not a goal.

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And if you watch soccer, you

know, sometimes a very intense,

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uh, how do you call it?

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Uh, attack.

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It looks very similar to a goal.

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So we also need to label that to train

a model, to learn this is attack.

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This is not a goal.

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So a fun story is because

the data came to us.

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We're not labeled.

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So me and my coworker spent two days.

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Just looking at those clips to label

whether this is goal or it's not a goal.

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So over the two days, I think I probably

watched hundreds of soccer goals.

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I don't want to watch soccer

for the next couple of years.

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So that's the unexpected

part of data science.

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Sometimes you need to do a lot of those

type of data quality check, labeling.

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But you have to do those type of things

because we, we label it in a very specific

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way that we know how to train a model.

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Eventually, uh, we used, uh,

we experiment that on a few

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different, uh, video analysis,

uh, modeling called, uh, um, I3D.

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So basically it's an inflated 2D.

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Using, uh, inflated 2D modeling to

analyze, uh, the data and the way,

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how to simplify the business problem.

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Because like I mentioned, after

tech, there could be a goal.

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It could be not a goal,

maybe ended in like a corner.

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For example, we'll just simplify

that into a binary problem.

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That's also, um, important way to tackle

a very ambiguous, complicated problem.

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Sometime you might not, you

might need to reduce the scope.

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Uh, for example, this, in this case,

we reduce the problem space from a

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multi class classification problem

into a binary classification problem.

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And then we train a model.

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When we came out with a classifier, uh,

with a classifier, we Use a classifier

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to run through the entire game.

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Every five seconds, we run through that

classifier and then see whether there

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will be a goal in the next few seconds.

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We also created a very fun demo.

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Basically, in real time, you can see a

Uh, likelihood score of whether there

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will be a goal in the next few seconds.

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It can make the viewing

experience more exciting.

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Avery: Yeah.

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I saw, I saw the demo actually, and

maybe I'll, I'll insert a little

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recording because it was pretty cool.

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Um, but what, what a cool project.

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Uh, and I think.

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Uh, there's so many

different, different things.

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I think that listeners can, can learn

from that one companies like Amazon.

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And honestly, a lot of companies are act

as consulting companies a lot of the time.

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Uh, and so what, like a gambling

company in this case, or any other

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sort of manufacturing company,

or I don't know, whatever.

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Company that exists a lot of the times

they like kind of outsource their

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analytics and data structure stuff to

smarter companies like, like Amazon.

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And I think that's, that's good to know.

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And I think that's a cool role to sit

in is basically you get to do analytics

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for, for multiple, multiple companies.

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I think that's really cool.

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And then the other thing I love that

you said was, you know, I didn't seem

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like you were too much of a soccer fan

necessarily, and you to become one.

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Uh, and that's sometimes what you

have to do is like, you maybe don't

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have the domain experience, but.

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You can kind of need the domain

experience when you're building machine

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learning models a lot of the time.

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Daliana Lui: Yeah, exactly.

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And I also worked on a football, American

football project when I was on the team.

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I knew, I mean, I, I know a little

bit of soccer, of course, but I knew

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nothing about American football and I

have to buy a book to read how football

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work, uh, to, to, you know, to our

point, to understand the context.

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Avery: It's crazy because yeah, it's

just, there's the cool thing about

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analytics and data science and machine

learning is it's really industry agnostic,

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which really means that you can take

the principles, the machine learning,

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um, models and the machine learning

algorithms and apply them to really

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so many different business problems.

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And so you could probably spend your

whole life just learning about different

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industries and how to apply just one model

to those, those different industries.

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Uh, which I, which I think is fascinating

and one thing I want to give you

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credit for in your LinkedIn content.

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A lot of the times you're,

you're obviously very technical.

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Like you use a lot of very fun buzzwords,

uh, when you're kind of explaining that

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and you've obviously worked for Amazon.

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So you're obviously very technical, but

one thing I really appreciate about your

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LinkedIn posts is, you know, sometimes

they're technical, but other times they're

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like, Hey, you as a technical person

actually kind of get more done when

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you focus on your non technical skills.

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Has that been true for you in your career?

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Daliana Lui: Yeah, absolutely.

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Uh, I think a lot of times we

think it's important to constantly

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grow your technical skills, but

that only get you, um, somewhere.

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And after that, uh, I wish

I could show you a plot.

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So basically if you imagine the career

trajectory from junior data scientists

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to senior data scientists, and later

staff and principal scientists, you'll

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see the requirement for technical skills.

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Slowly, the increases.

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Not that high, and however you require,

it requires more communication skills,

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leadership skills, influencing skills,

higher level you want to become.

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And I think once we get into the

reality, there's no homework anymore,

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and there's no, uh, perfect data.

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And a lot of times the stakeholders are

not even clear about what they want.

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And so it is essential to know.

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You know, from the beginning of the

project, how to ask the right questions,

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how to work with the right people,

how to find a project that actually

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have the high impact that can get you

a promotion and later on, how do you

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influence the right stakeholders to

get your solution in the right place?

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Avery: It's a, it's a crazy concept

because I think we, we like to think

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as technical people, the, the more

technical you are, the more you'll get

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paid, the more desirable you'll be, the

more influence you'll have at a company.

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And, and to be honest, it's just not true.

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Even if you're not junior levels,

even when you're trying to get hired.

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It's not like the smartest person or

the best person at SQL lands the job.

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There's often these soft skills, these

people skills, these communication

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skills, uh, that come into play and,

and really kind of make the difference

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between maybe a good data analyst, a

good data scientist, and a great one.

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Um, one of the ones that you posted about

recently, and I think you kind of just.

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Hinted at it just barely.

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And your answer was sometimes

these stakeholders don't have

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a clue, uh, of what you want.

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And so one of the things you

posted recently was like, one thing

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that can make you a great data

scientist is getting feedback early.

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Can you expound on that?

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Daliana Lui: Uh, when I started,

uh, in Amazon, I wanted to show

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my manager where my stakeholders,

my work only one is perfect.

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Otherwise I would feel embarrassed.

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But reality is.

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Sometimes you think you understand

their request, but you don't.

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Or during the time when you're working

on a project, their preference,

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their priority have changed.

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So it's important to constantly

align with your stakeholders to make

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sure you understand their needs.

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And also, there's only It's very

limiting what you can communicate

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three words, especially you're

working on a data science project,

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whether you need to turn that into a

dashboard or machine learning model.

302

: 00:17:04

So you have to show them your demo.

303

: 00:17:07

Um, I, in my career growth

course, I always talk about

304

: 00:17:11

show them a ugly demo first.

305

: 00:17:13

Even if it's just in your, um, Jupyter

notebook or in your, you know, SQL,

306

: 00:17:19

uh, you know, editor, show them to let

them know what's the, uh, what does

307

: 00:17:24

the MVP look like, it's even better

if you can create a very small UI

308

: 00:17:29

so they can play with, they can get

excited for, and when they see what.

309

: 00:17:35

It might look like it gave them more idea.

310

: 00:17:37

So it's not a bad thing when they tell

you, Hey, this is not what I want.

311

: 00:17:41

If you're only 20 percent of the

project, but it will be a huge problem.

312

: 00:17:46

If you're already at 80 percent

of projects, actually you want to,

313

: 00:17:50

uh, have those small tweaks and ask

them, Hey, is this what you want?

314

: 00:17:54

Or I have a few other ideas that

I think that might help you.

315

: 00:17:58

This is my proposals.

316

: 00:17:59

What do you think?

317

: 00:18:00

So have those conversations

early can save you a lot of time.

318

: 00:18:04

When you're towards

the end of the project,

319

: 00:18:07

Avery: I'm sure, I'm sure you've seen

this now as you've grown in your career.

320

: 00:18:10

And I've seen it as I've grown in my

career to the point now where I, you

321

: 00:18:13

know, I code a little bit, but a lot

of what I do is, is directing other

322

: 00:18:17

people to code and stuff like that.

323

: 00:18:19

And I realized that, you know, now I'm

the stakeholder and I've become the, a

324

: 00:18:23

bad stakeholder where I don't even know

what I want half the time, what I'm

325

: 00:18:26

asking, or when I do know what I want, I

kind of stink at explaining what I want.

326

: 00:18:31

Um, and so when people.

327

: 00:18:32

You know, who, who are working

under me, are able to come back

328

: 00:18:35

with something quickly and be like,

Hey, is this what you're asking?

329

: 00:18:38

Uh, especially like in a meeting or in

a demo, like a loom video, uh, because

330

: 00:18:42

I like, like what you said, you,

you can only say so much with words.

331

: 00:18:46

It's almost like, like

internet speed, right?

332

: 00:18:48

That's basically like how fast information

can transfer words is like, I don't know.

333

: 00:18:52

15 megabytes per second, but

like an in person meeting,

334

: 00:18:55

we're talking like gig speed.

335

: 00:18:57

Um, there's just so much

more communication, which is,

336

: 00:19:00

which is, which is awesome.

337

: 00:19:01

And that ultimately leads to

what, what's called like adoption

338

: 00:19:04

and people using your analysis.

339

: 00:19:07

Um, and that's another thing that you've

mentioned, uh, on LinkedIn that like.

340

: 00:19:11

You can't really do data

science just for, for funsies.

341

: 00:19:14

You have to get it adopted.

342

: 00:19:15

Can you talk a little bit more about that?

343

: 00:19:17

Daliana Lui: I think there was a data

point a few years ago, probably over 80

344

: 00:19:22

percent of machine learning models fail.

345

: 00:19:25

I think part of it is natural

because there's a research or

346

: 00:19:29

discovery nature in data science.

347

: 00:19:31

Not everything has to

be put in production.

348

: 00:19:34

But a lot of times if What

you have done become useless.

349

: 00:19:39

Then from the company perspective,

they wasted their time.

350

: 00:19:43

You don't have direct impact.

351

: 00:19:45

And from a personal growth perspective,

if you don't have the impact, it's

352

: 00:19:49

hard to define your contribution

to the team, to our growth.

353

: 00:19:54

And how do you advocate?

354

: 00:19:56

Yourself for that promotion, when

you build something, a lot of data

355

: 00:20:01

scientists and also engineers, they want

to just build something that they think

356

: 00:20:06

is important or they think is cool.

357

: 00:20:08

They just learn some model

from a Coursera course.

358

: 00:20:11

They want to implement that.

359

: 00:20:13

I think that's a great way to learn.

360

: 00:20:15

By doing, but when it comes to, um, doing

work for a, most of the time for profit

361

: 00:20:20

company, you need to think about is what

I'm working on aligned with my team goal.

362

: 00:20:26

I'm not helping my stakeholder

or, um, this is the five goals.

363

: 00:20:30

My manager tried to achieve.

364

: 00:20:32

I'm not helping my manager.

365

: 00:20:33

I might be a team player.

366

: 00:20:35

It's better if you can align

your passion to the impact.

367

: 00:20:39

And sometimes the passion and

impact might be a separate thing.

368

: 00:20:44

There is one thing maybe you can exercise

your passion for, for learning on your

369

: 00:20:48

own time or take 20 percent of, you know,

your, your work time, but make sure the

370

: 00:20:54

80 percent of your time, you're actually

solving the useful business problem.

371

: 00:20:58

And a lot of time, it

could be a little bit, um.

372

: 00:21:01

Boring and repetitive.

373

: 00:21:03

Um, I think that's also an opportunity

for you to create more impact, to

374

: 00:21:07

see how can you, um, automate this?

375

: 00:21:10

How can you also, sometimes you need to

motivate yourself that again, aligning

376

: 00:21:15

with the stakeholders, with the customer's

pain point, stakeholder's request.

377

: 00:21:19

Sometimes if you see how does that

implement it, how it solve even

378

: 00:21:23

just one person's problem, it can.

379

: 00:21:26

Also make you feel more motivated

to work on projects like that.

380

: 00:21:30

Avery: It's hard because in, in data,

especially like in school, right?

381

: 00:21:34

Let's just take like a normal, you know,

college, maybe like a master's degree

382

: 00:21:38

or maybe even an undergrad degree.

383

: 00:21:39

Your master's was in what again?

384

: 00:21:41

What did you say your master's was in?

385

: 00:21:42

Daliana Lui: In statistics.

386

: 00:21:43

Avery: Okay.

387

: 00:21:44

And yeah, and my master's was in,

was in data analytics technically.

388

: 00:21:47

Right.

389

: 00:21:48

Um, but like, I would imagine it

was the same in your master's, but

390

: 00:21:51

my master's was very theoretical.

391

: 00:21:53

Um, and it was all about like, like, for

instance, you, you might be interested in

392

: 00:21:58

statistics of, of like getting a P value

less than, you know, zero point or 0.

393

: 00:22:02

05.

394

: 00:22:04

And you might be interested in like, okay.

395

: 00:22:06

Like, can we make the P value lower?

396

: 00:22:08

I know we can't really make P values

lower, but like you might be interested

397

: 00:22:10

in really low P value or, or in

like my masters, it might be like,

398

: 00:22:14

Hey, we're like 79 percent accurate,

can I get to 81 percent accurate?

399

: 00:22:18

So we're thinking in like

P values and percentages.

400

: 00:22:21

But really, like you said, most

businesses are pro for profit.

401

: 00:22:25

So they think in dollar signs and

usually pretty much dollar signs only.

402

: 00:22:30

Uh, so if we can't relate our

analysis and our work that we've

403

: 00:22:33

done into dollar signs, and it

doesn't have to be dollar signs.

404

: 00:22:36

It can be time saved.

405

: 00:22:37

It could be lives saved.

406

: 00:22:39

It could be.

407

: 00:22:40

You know, people promoted, I don't

know, whatever, whatever the,

408

: 00:22:43

the key units, yeah, more users.

409

: 00:22:46

That's, that's another good one.

410

: 00:22:47

Whatever your team is focused

on, you have to figure out how

411

: 00:22:50

to get your analysis there.

412

: 00:22:51

Otherwise you're not really

helping the team out one and two.

413

: 00:22:54

Like you said, your, your career

growth is going to struggle because.

414

: 00:22:58

Especially these bigger companies,

like your promotions are kind of tied

415

: 00:23:02

to the work you've done for the impact

you've had for the business, basically.

416

: 00:23:07

Daliana Lui: Yeah.

417

: 00:23:07

Avery: Yeah.

418

: 00:23:08

Okay.

419

: 00:23:08

I, I agree with you there.

420

: 00:23:09

I think that that makes a lot of sense.

421

: 00:23:11

Another thing I think you, you

mentioned in a LinkedIn post is like.

422

: 00:23:14

Maybe you are trying to do that, right?

423

: 00:23:16

But you're like, you're kind of struggling

to, to like advocate for yourself.

424

: 00:23:21

You're kind of struggling talk to

your, to make, to make your work clear.

425

: 00:23:25

Do you have any advice on like

how to like make your, your work

426

: 00:23:28

more known like in the company?

427

: 00:23:30

Daliana Lui: Um, yeah, so.

428

: 00:23:32

Uh, you meant, uh, making, having

more visibility in a company?

429

: 00:23:37

Avery: Yes.

430

: 00:23:38

Daliana Lui: Yeah.

431

: 00:23:39

If you already work on a high

impact project, you probably will

432

: 00:23:42

work on sometime directors or VP.

433

: 00:23:46

So I don't think you need to be kind of

quote unquote famous in your company.

434

: 00:23:51

Of course it helps, right?

435

: 00:23:52

If you did deliver a high impact

project and you give a talk.

436

: 00:23:56

You have more visibility.

437

: 00:23:57

Maybe there are other people come to,

um, invite you to for collaboration.

438

: 00:24:03

For example, when I published a blog

post on the soccer project, which is

439

: 00:24:06

talking about there are other teams

reaching out to me, asking questions,

440

: 00:24:11

looking for collaborations, but a lot of

times you only need to be visible to a.

441

: 00:24:18

In their circle of people, for example,

the people who actually decide the road

442

: 00:24:23

map of the team or the person who might be

on the committee of your promotion review,

443

: 00:24:31

I think a great way to do this is to.

444

: 00:24:34

See if you can build a relationship

with them to collaborate with them.

445

: 00:24:38

And the first step is, uh, if you,

again, don't know how to build a

446

: 00:24:42

relationship with, you can go from a

perspective to just learn from them,

447

: 00:24:47

to get feedback and show them what's

something you have been working on.

448

: 00:24:51

Kind of similar to, we talk about

getting stakeholder feedback.

449

: 00:24:54

Um, if you want to bring more awareness

for your project, for example, you're

450

: 00:24:59

building a new tool that will improve.

451

: 00:25:00

You aim to improve your team's

productivity, maybe talk to the key

452

: 00:25:05

users, potential users of this tool or

some other stakeholders and show them

453

: 00:25:10

a quick demo, um, ask them what's their

pain point and get, get a user feedback.

454

: 00:25:15

So when someone is.

455

: 00:25:16

Involved when they, uh, give you

ideas and you implement them, they

456

: 00:25:21

feel they're part of the project.

457

: 00:25:23

So later on, when they have some similar

project, they're aware, Oh, there is

458

: 00:25:27

someone I can talk to on that team.

459

: 00:25:30

They're expert in this.

460

: 00:25:31

So in a company, you don't

have to be an expert.

461

: 00:25:34

You don't need to work on one project for.

462

: 00:25:37

10 years and have a PhD

in it to become expert.

463

: 00:25:40

Sometimes if you deliver project end to

end, you, you know, a lot of the domain

464

: 00:25:46

knowledge and the business contact,

you are an expert, let people, uh, by

465

: 00:25:50

collecting feedback, um, talk to people

one on one, um, sometimes help them.

466

: 00:25:55

People know that you are

the expert on this domain.

467

: 00:25:58

And when you finish the project,

share your work through an internal

468

: 00:26:03

blog post, or you can schedule a

lunch and learn session, et cetera.

469

: 00:26:08

And, uh, I know we all

have our own priorities.

470

: 00:26:12

We're busy, but sometimes also need to,

you can set aside some time to host.

471

: 00:26:16

Office hours or, um, Q and a

sessions, be generous with your time.

472

: 00:26:21

Sometimes also goes a long way.

473

: 00:26:23

Avery: Very cool.

474

: 00:26:24

And I think, I think that is

awesome advice on, on increasing

475

: 00:26:27

availability, uh, sharing your work.

476

: 00:26:30

It's such a, seems, seems like you

shouldn't have to do that because

477

: 00:26:33

you're at work and it's like, why

do I have to share this with anyone?

478

: 00:26:35

Uh, but it can be such a big,

uh, impact to your career.

479

: 00:26:39

And, and others as well.

480

: 00:26:40

Uh, well, Dalyana, this is the

Data Career Podcast, and obviously

481

: 00:26:44

you've shared a lot of good things

about growing your data career.

482

: 00:26:47

Uh, I want to ask you if you had to

give someone who, you know, is listening

483

: 00:26:50

to this episode, any sort of advice on

advancing their career to the next level.

484

: 00:26:54

What would you give them?

485

: 00:26:55

Daliana Lui: I have so many devices,

very hard to come down to one.

486

: 00:27:00

Yeah, I would say.

487

: 00:27:01

There is, of course, it's important

to understand how to create more

488

: 00:27:05

impact for your company, uh,

how to advocate for yourself.

489

: 00:27:09

We are, um, in this kind of system,

there's promotion, there's annual review.

490

: 00:27:16

It's important to know

how to play that game.

491

: 00:27:19

Uh, but at the same time, it's

also important to look inward.

492

: 00:27:23

To know what do you enjoy, what is your

goal, uh, what's your life goal beyond

493

: 00:27:29

your, the, the next level of the promotion

or the raise, I think is helpful for

494

: 00:27:34

you to play the long game, um, when

you know yourself better, so maybe.

495

: 00:27:41

Uh, every quarter or every year said,

uh, we're at the end of the year,

496

: 00:27:45

maybe during the holiday season said,

uh, one afternoon, just write down how

497

: 00:27:50

do envision your life will look like.

498

: 00:27:52

And then think about how could

your career, your family, your

499

: 00:27:55

friends play a part of it.

500

: 00:27:57

So at the end of the day, the career

is only one aspect of our life.

501

: 00:28:01

Avery: I think that's important to

remember because it it's really easy to

502

: 00:28:04

get lost in, uh, all in it all because.

503

: 00:28:07

It's like, why do we work?

504

: 00:28:09

We work to live.

505

: 00:28:10

And sometimes it feels

like we live to work.

506

: 00:28:12

Um, so I think that is sage advice.

507

: 00:28:15

Uh, Dalyana, thank you

so much for coming on.

508

: 00:28:18

We'll have all of Dalyana's, uh,

links in the show notes down below.

509

: 00:28:21

She's been working on

something really cool as well.

510

: 00:28:23

Dalyana, you want to talk about

what you've been doing recently?

511

: 00:28:25

Daliana Lui: Yeah, so I'm working

on, uh, more career coaching.

512

: 00:28:30

So one course I recently

launched is called the data

513

: 00:28:33

science career accelerator.

514

: 00:28:35

So we talked about how to, uh, improve

your stakeholder management skills.

515

: 00:28:42

How to be a great communicator.

516

: 00:28:43

So all the soft skills we just talk

about and how to build a relationship

517

: 00:28:47

with our manager, how to create more

impact and get a promotion you deserve.

518

: 00:28:52

So basically we teach you all the

required soft skills, leadership

519

: 00:28:58

skills, communication skills

that school didn't teach you.

520

: 00:29:01

And this course requires you

to be a data scientist for,

521

: 00:29:05

you know, at least one year.

522

: 00:29:08

Um, and, uh, a lot of, uh, the senior

data scientists take this course too.

523

: 00:29:12

They want to learn how to

continue to expand their scope.

524

: 00:29:16

So, um, I will share the

link with, um, Avery.

525

: 00:29:19

Avery: Yep.

526

: 00:29:19

We'll have the link in the

show notes, uh, down below.

527

: 00:29:22

We'll also have links

to your social as well.

528

: 00:29:24

So make sure you're

following Dalyana already.

529

: 00:29:27

Dalyana, thanks so much

for being on the show.

530

: 00:29:29

Daliana Lui: Thanks Avery.