so a few weeks ago I was visiting in
California when I was there I had a
chance to interview Joma tech and just
in case you don't know much about him he
went to the University of Waterloo in
Canada for a computer science degree
while he was there he did a bunch of
internships in software engineering data
science and p.m. companies like LinkedIn
Facebook and Microsoft and after
graduating he worked as a data scientist
at BuzzFeed and Facebook and now he's a
youtuber too
now in this part of the interview that
I'm gonna show you now we basically
talked about first of all what it's like
to be a data scientist and also how to
become one so let's just jump into it
and the first question I asked him was
what was he like to work as a data
scientist at BuzzFeed and Facebook so I
could talk more about BuzzFeed maybe a
bit less well Facebook sure but yeah so
at BuzzFeed I was basically hired to
understand like how to do how to
distribute videos on Facebook so how it
works is like wise you know BuzzFeed is
like a media company they make a lot of
videos they make a lot of videos
everywhere and one of the platforms that
they care a lot about is Facebook so if
you've ever seen like a tasty video with
the hands and stuff that's that's
BuzzFeed so basically my job was to try
to understand our Facebook strategy you
know they have a lot of questions like
you know should we share or should we
cross posts which is very like specific
to like Facebook and stuff like that and
then also like what kind of videos do
better than others and stuff like that
so basically my job is is to basically
like a product analyst for Facebook
videos and then a lot of times what we
would do is we would just look at a lot
of these videos and try to understand
like or why certain things happen why
did we get more shares than usual and
then yeah that's pretty much it and then
at Facebook at Facebook I like the video
team and basically what we're trying to
do is we're just trying to make videos
more successful on Facebook yeah
so it sounds like your career like your
entire career humming all about videos
yeah pretty much so that's that's like
pretty much my choosing it's because
even before starting at BuzzFeed I was
always into like videos and it's not
like very good but I was pretty like I
really want to be a YouTube celebrity
for some reason you know as it's a very
vein of myself but yeah I even chose
like my career based on that and yeah
like I chose BuzzFeed because I thought
that I might be able to be in their
videos in LA and then maybe I get some
recognition and then using that
recognition I could grow my youtube
channel
that didn't happen by the way so yeah I
actually went to BuzzFeed for that yeah
and then when I went to Facebook my
initial thought was not to be on the
video team because I was like okay I'm
done with videos because when I switch
to Facebook I didn't want to do anything
with videos anymore but it was just so
tempting because the team was like oh
the creator team I was like oh my god I
was like it sounds it was almost like
not destiny but it was like my calling
so I was like okay fine I'll go on that
team because on Facebook you you're in
bootcamp first and then you get to
choose what team you want to pick later
so I didn't initially want to be in
videos but India and I still ended up
being there yeah so you said that you
wouldn't be able to share that much
stuff about the work you did at the
moment
yeah cuz yeah some of this stuff is
still like like not public or also like
they don't want to tell that's like the
strategy to especially YouTube and stuff
like that but um yeah I could talk about
it vaguely though okay
let's a focus on BuzzFeed okay so are
there any interesting examples of data
science work you did Buzzbee yeah um I'm
gonna be quite honest here sometimes I
think like not every company needs a
data science team
and BuzzFeed was kind of on defensive it
especially from what I was working on
because like it's kind of like like
running a youtube channel or like
running a Facebook channel essentially
I'm an analyst for their Facebook
channel and a lot of the times they're
you can't really explain stuff with data
with stat sig you know like one of the
questions was like oh you know should we
share should we cross post which one's
better which will get more views but the
issue is you have to you know work with
the people who actually manage the page
make sure they post at the same time as
same everything so basically we did a
lot of like crack the a/b test like it
wasn't a claim a/b test to try to
discover if one thing is better than the
other and we were cared about like is
sharing better than cross posting and
stuff like that and then another kind of
work we did at BuzzFeed was just
understand the health of our Facebook
pages and one of the things like it's it
doesn't even require that much data
science skills or anything but we just
looked at you know how many views are we
getting like we saw that our page like
tasty they were getting AG less and less
views per video so meaning like we were
making a lot more videos just to get the
same amount of view as we did before so
then we have to tell that the content
team saying like oh by the way this
might not be working we need to think of
different strategies to get more views
so it was very similar to like what I do
for my own YouTube channel we're just
like looking at various videos and
various like trends and then try to
capitalize on that so that doesn't sound
too technical yeah it's not that
technical I mean to be honest like at
BuzzFeed I mean there were some other
things that are more technicals like
liking and I automated there like
dashboards I automated like emails and
stuff like that because they were pretty
behind in terms of like automation and
infrastructure so sometimes I what I
would have to do is like I would have to
like write slack bot slack bots for
whenever something happens I have to
tell like this like bow has to tell like
the content team and stuff like that so
I would guess that's technical because
you know you have to code a little bit
right but ya know like in terms of
technical is it's not that technical but
even at Facebook it wasn't that
technical
okay oh I did it was like sequel sequel
queries right and then he was like
tableau yeah to like do visualization
and then if I needed like to do like a
simple linear regression or like a
logistic regression then I would use
like our Python but that's it
right yeah is there anything more you
might be able to share from Facebook I
think well I guess for Facebook like I
could say that we have a lot of data and
we have a lot of data and it makes
things a lot easier cuz like we have a
lot of data engineer so like things are
really really easy to do now all I need
to do is like write sequel query and I
could query like billions of like data
points just in one query and then yeah
that's pretty much it and like the job
that I did at Facebook we basically
wanted to make creator successful so you
know you could you could kind of like
assume you could kind of like and for
what I did but basically we look at our
platform Facebook as a whole and then
try to understand how to change the
platform such that we can make craters
kinda like YouTube creators successful
in our platform yeah so I guess one like
hypothetical example might be like oh
yeah live video versus regular video
mm-hmm yeah that could work too it's
like we could try to like compare two
different types of videos and understand
which is better and then if we're sure
that one is better we could tell like
our partners to say oh by the way you
should do this more it will increase
your distribution so yeah we would get a
lot of understanding of our own
distribution ecosystem and then tell our
partners like what to do we're gonna say
the partners my video creators yeah like
video creators like for example you know
like um like BuzzFeed it's a partner
basically anyone who's big on Facebook
you could kind of guess that they're
partners meaning that we have at least
like a few people talking to them all
the time
yeah okay mmm-hmm all right another
question I have for you was how does
your getting to data science yeah
so I think so so I did do computer
science as my major and then I want to
be a software engineer I didn't think
about data science
and then at one point I remember I was
that LinkedIn we were like nine interns
all from Waterloo we all live together
and then at one point I remember one guy
want one friend he said like software
engineering isn't that great
I was like wait what it means like I
think I would rather like if I got paid
the same I think I would rather wash
dishes than be a sovereign Jim I was
like oh my god really is it that bad you
know but then that kind of like they're
kind of stuck in me you know and and and
the more we talk about it the more
understood what he was talking about so
he was talking more like as a software
engineer he feels like he doesn't has it
he doesn't have as much say in the
product direction as he wants so
coincidentally a lot of people who
interned at LinkedIn with me they all
want it to be like product managers or
entrepreneurs or stuff like that and
then I guess I also want to be a like a
startup guy back then and what's closest
to like leadership you know because I
thought it was cool or it's like all
leaders and stuff like that usually PM's
are like leaders and they usually end up
getting into more senior like executive
roles so then I wanted to do p.m. and
then when I applied for jobs I realized
that data science might be a little bit
closer to p.m. and then that's how I
started in data science because and also
I just asked my friend on my okay and
refer me to data science position and my
friend referred me and then that's how I
got my first internship and then that's
how I discovered like oh you know data
science is not about like a machine like
it's not always about like machine
learning or like deep learning stuff
like that a lot of it is like a business
intelligence job so yeah that's how I
got into it so you were a software
engineer at LinkedIn and then you got
interested in p.m. and their science and
you sort of apply for those jobs
basically did you do anything to prepare
for their science interviews yeah I
learned sequel because I never did
sequel and I also learned
probability questions you know Bayes
theorem and yeah and I kind of brushed
up on stats like I did learn some stats
in school but you know I I don't
remember everything so I have to like
just read up on it and then obviously I
went on the glass door I checked like
what kind of questions they asked and
then yeah so that's how I prepared for
the interview like I knew that it would
be easier in terms of like coding or
like a technical whiteboarding interview
I knew that would be easier and I was
right so I didn't need like that much
preparation cuz I already prepared a lot
for like software coding jobs yeah so
going through those topics was actually
helpful for those Antibes oh yeah for
sure good cuz I literally did not know
sequel okay yeah so if someone you know
wanting to get into data science today
mm-hmm how would you recommend that they
prepare yeah so kind of like it really
depends on what they mean by data
science but if you mean like my data
science like data analytics then I think
we should do or you in Sulphur are we
pretending that this person is in
software and who wants to switch to uh I
think this person could be either in
software okay
something else god I got it okay so I
guess yeah using my exam previous
examples of like you know buzz for in
Facebook I guess for buzz actually not
both them is quite similar to interview
process so I think what you need to do
is first yeah learn sequel if you don't
know that already it should be pretty
simple you should just like look it up
on the internet do a few sequel
questions and you're good and then you
should learn to code a little bit like
Python for example because they will ask
you like maybe one or two easy Python
question they're not as hard as like
software engineering ones and then make
sure you understand the basics of
probability and stats stats mean like do
you know what a confidence interval is
like very basic do you know what a
normal distribution is do you know how
to do like yeah just if knowing how to
do confidence intervals and then knowing
your
like distributions like binomial
distribution plus all and stuff like
that because sometimes they might ask
that and then that's it yeah like okay
that would be pretty good enough for an
entry level right oh yeah one more thing
yeah but I didn't study for that but you
have to have like product sense mm-hmm
so learn about the product or the
company that you work that you will work
for and try to think about like oh how
would that look how would I look at the
data to find something that's insightful
that would help the company you know so
if you practice these mental exercises
then you'll be good for the interview
okay mm-hmm so you said see called
Python probability statistics and
product test exactly
are there any particular resources that
you would recommend for learning those
yeah the great thing about bigger
companies is that they would send you
like a lot of links to where you can
study for each things I don't remember
sequel where but basically sequel they
gave me a website where I could practice
like sequel questions but there's tons
and then for probabilities I did a lot
of brilliant dot-org questions oh yeah
we're not sponsored this time but oh
maybe you can be but yes I use brilliant
org for all your probabilities questions
okay stats Wikipedia I just want to
capilla and like refresh my mind and
maybe my old notes in school and then
for product sense
I usually just look at I actually ask
friends you know or people who work in
consultant companies they're usually
pretty good I like product sense or like
doing these kind of things so yeah
brilliant or some sequel site and also
you know Wikipedia okay mmm-hmm okay so
I feel like that that's good for after
getting the interview oh right oh right
okay so for getting an interview yeah so
I guess my next question is what should
people do to encourage their chance of
getting the job getting dinner sizing
TVs right so I think I think it's better
to have like if you do have like a
technical internship that's
pretty good then you will already pass
the technical part so having one
technical internship that'll be great if
you don't have that you have to do side
projects I'm not sure if data like data
boot camps are like worth it or not
because I've never done it with like
I've never seen people without it that
don't have like a technical background
but doing projects that are stats
related or like technically related then
that's pretty helpful too like an
example would be like grab a random data
set on the internet there's one popular
one it's like the New York train station
data set and then make a presentation
out of it make a project find out like
oh what's the busiest station where
should you take is like where should you
take the bus blah blah blah there was
also like also another example that I
could say like you could go to SF some
SF website accept government website you
could look at how many how many like
crimes happened around SF like one fun
thing you can do is like you could look
at how many bikes were stolen where and
then you know and then you could give a
recommendation of where to park your
bike to not get it sold in all right so
that's like a very simple data science
data science project I can do so yeah so
once you have that in your resume make
sure also that your keywords have the
words like you know ipython notebook are
yes equal basically all the buzz words
that they use and then you should get
the interview like that nice do you have
any advice on how to write a resume for
those positions yeah for me I think it's
kind of hard for me to get that vice
because I got it easy because I just got
an internship at like a big company and
then I'm set right but if not then yeah
definitely highlight those like sequel
like all those text acts that you need
on like in the beginning and then yeah
make sure your list your projects make
sure you actually tell them like exactly
what did you do in that project and why
is it useful because I think like for
data science they always want to see
that the things you do have an impact
that you know that you don't you can't
just do exploratory analysis but it's
useless so yeah make your resume
one-page usually because then some
recruiters don't like to pages I heard
and yeah it should be pretty good okay
so that last question I have about data
science is are there any common
misconception about data science or did
a scientist or something that people
tend to misunderstand yeah I think I
think the biggest misconception is just
the job itself like data science just
because we shared the word data science
a lot for a lot of different roles like
for example machine learning engineers
sometimes we call them data scientists
sometimes we call core data science
which is more like PhD level data
science they usually do not only product
focus data science but like something
that's more inject like they build more
complex models and then there's also
like product analytics which is what I
do but they also call it data science so
I think the biggest misconception is
like mixing those three up and then kind
of assuming that you need a PhD to get
into data science that you need to learn
machine learning or you need deep
learning experience so I think that's
the biggest misconception and I think
one advice for me is to it's just stuff
like I want people to know that you
should really try to understand what
exactly your role is before you say you
want to do that job because you know
it's very possible that you always
thought that you want to do data science
but then once you get into the industry
imagine you if you get a data analytics
position and you realize like what the
[ __ ] I mean what the hell is like I
don't want to do this you know this is
kind of lame so I just want you to make
sure that for people who do want to go
in data science I want you guys to know
exactly what your
into and to make sure that you actually
like data science because of that not
just because it's so hot right now and
everyone said it's great yeah all right
so those are sort of all the questions I
had about Nina science but is there
anything you want to add to this
conversation that we okay that's fine
yeah yeah I think data science like in
general just like my two cents is that
data science is great I think it does
take some creativity especially like my
data science which is product analytics
data science and it really helps you
like in your soft skills and the
deafening makes you grow as a person
because you constantly you work of like
pans and like engineering managers so
you start to learn like how people work
in real life and you realize that it's
not always the case that if you're more
technical or more intelligent that
you'll succeed sometimes things are a
lot more nuanced you know so you need a
lot of soft skills you need to be you
need to be in general just a good worker
and my intern manager used to say that
it's hard to hire for like good data
scientists because you could screen for
intelligence you know by giving like
probability questions and stuff but it's
hard to screen for like laziness and and
that's usually the biggest like false
positives of hiring someone is that you
realize like oh they're actually kind of
lazy they just don't want to do things
and that's yeah that's usually like the
hardest thing to screen for but yeah
like India and like if you're a data
scientist p.m. or like software engineer
even if you're not the most technical
one or even you if you're not the
smartest in class right now at school
don't worry about it because that might
not determine how good you are you will
be at your job you know if you have like
the grid and the ambition I think you
might do well at any position
yeah alright so that wraps up this
conversation and as I mentioned earlier
on the screen this video was sponsored
by brilliant org it's a website that
helps you master mathematical and
computer science concepts through
problem solving so what Joe mentioned
earlier is this probability course and
one example of the problems covered in
this course is this one this says which
of these events is most likely to happen
when flipping a fair coin flip two or
more heads when flipping three coins
flip twenty or more heads when flipping
thirty coins or flip 200 or more heads
when I'm flipping 300 coins so I think
this is actually pretty cool because
it's pretty similar to the types of data
science interview questions that you
might actually get and there are other
courses that cover different topics
through problem solving too for example
computer science fundamentals algorithms
Python and even machine learning so if
you want to get started with any of that
use my referral link and the description
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20% of their premium subscription anyway
thank you as always for watching my
videos and I'll see you guys in the next
one