I get paid a hundred thousand base salary
like I'm hired as a new grad
Hey guys welcome to this episode of
Reality vs Expectations
where I get people from different careers
and I have them write about five cards of what they
thought their career was gonna be like
versus what it's actually like
today I got Joma
currently his a Data Scientist at Facebook
previously he has worked at LinkedIn
Buzzfeed and Microsoft
he graduated from the University of Waterloo
with the degree in Computer Science
lets get this video started
Hey guys today I got Joma
he works as a Data Scientist at Facebook
but Joma can you tell me
the story about how you became a Data Scientist?
Yeah sure
so I did a lot of internships when I was in college
I did some Software Engineering internships
and I also did a Data Scientist internship
at Facebook
and then after that
I worked fulltime at Buzzfeed as a Data Scientist
and then finally I came to Facebook as a Data Scientist
can you walk me through a day in your life
as a Data Scientist?
Yeah sure so mostly what we do is
we come into work
and then usually we have a lot of meetings
because we have to talk about 
what are like the next goals
and what metrics to track for our team
so for example I work for videos
at Facebook
and basically what I do is I query a lot of
querries getting some data
to try to make decisions for the PMs
give me an example of a specific type of data
that you're looking for
yeah sure like for example
you wanna see alright what countries are doing
like most well for our shows
cuz now we have a watch tab on Facebook
and we wanna know which countries are doing the best
and which countries should we invest in
and that's one way to look at it
and how did you become
qualified to get a Data Science job?
yeah so It's actually
a lot of different background
you could come from a lot of different backgrounds
my background is in Computer Science so
with the Computer Science degree
I was a little bit more technical
and then when I did the internship
because the internship
they allow anyone to get it you don't need to be technical
and that's where I learn how to do
Data stuff like for example basic stats
did you come from like extremely qualified school?
is that why your looking for internship?
I went to University of Waterloo
which is a Canadian school and they do a lot of internships
I wouldn't say its like the best school in the world
like its not ideally at all
but we do a lot of internships and maybe that's why
we get more preference over other schools
okay
so lets go to the Reality vs Expectations questions
what's your first card?
so my first card is
most people think you need a Ph.D
to be a Data Scientist
and that's actually a myth
because I don't have a Ph.D
I should just have a bachelor
and then at Facebook I've met many people
that have the Neuro Schience degree
or even someone that had a
Family and Sexuality degree
and most people come from consulting backgrounds
so yeah so you deffinitely do not need a Ph.D
I think the reason why peope are confuse by it
it's because when you think of Data Science
you think of the machine learning of Data Scientist
since they came from different backgrounds
how did they teach themselves
or how did they learn the skill well enough to get a job?
so theres two ways
either you do an internship
or you just study basic stats
because to be honest
its less about learning the fundamentals
or like being really good at the stats
to be good at this Data Scientist
you usually have to have more empathy
to be good at Data Scientist
because you have to ask the right questions
and then answer them thoroughly
because technically its not that hard
you only need to know some sequel queries 
maybe a little bit of Python which everyone can learn
what's Reality vs Expectation card number two?
yeah so this is a little bit related to the previous one
most people think Data Scientist
works so solely on machine learning
and like artificial intelligence
but that's not true
I just wanna talk about the three arc types
of Data Scientist
theres one
is Data Science analytics?
that's what I am
and then I'll talk about that later
and then there's Data Engineers
and then there's also Data Science Core
that's what people at facebook calls
so Data Science Analytics this is like us
we just like a Data
we do some sequel queries we process it we make graphs
and we communicate with to the Product Managers
and then Data Engineers
those are the one that retrieves the data
build the infrastructures
so we can actually look at the data
and then Data Science Core those people are like the
hardcore Ph.D with like recommendation models in forecasting
awesome, what's card number three?
yeah so card number three is
Data Scientist is just about
putting a bunch of data in a Blackbox model
and then I would just output an answer
so that's very not true because
what's most important in Data Science
is about you know like I said
empathy and also understand what the real questions are
I'll give you an example 
why you can't just put in a Blackbox
now Blackbox what happens is you give it input
and then you say what to optimize for
now imagine you have a video product
and you wanna focus on a specific country
in the emerging market
and then to help boost your video product
and then what you wanna optimize is time spent
it makes sense right because you wanna
make people watch more videos
and then you put in a Blackbox and it says oh
Vietnam or Thailand is the best country
but then that doesn't tell you the whole story because what if
the reason why they spent so much time
it's because their just spending time buffering
or loading the video
so that's why you can't just put things in the Blackbox
cuz you have to understand exactly what's happening
to the users on the other side
can you define Blackbox?
yeah so Blackbox meaning like
models that people pre create for example
a simple linear regression
or like a random forest or even
like a deep learning model
sometimes you can't solve problems just by
encoding data in these models
so that's why I mean by Blackbox
It kinda relates to
like everyone thinks correlation equals causasion
like a two things correlate they think it's causing
obviously this Data Scientist you know better that
just cause two things are correlating doesn't mean
that you know the causation
exactly so one of the biggest mistakes is you know
correlation vs causation
and you will always find a correlation
and then you would optimize on that certain thing
thinking that it would
benefit the other thing for example time spent
correlates with likes for example
and then what you see later on
is that maybe if you increase time spent
it doesn't necessary mean
the more likes you'll get cuz maybe you'll just get
wasting time spent and stuff like that
time spent there are lower quality
exactly, what's number 4?
you need to know Hadoop
Mapreduce and Spark
if you wanna be a Data Scientist
cuz these are like the buzzwords that you hear the most
and that's not true at all
cuz I've never written a Mapreduce job in my life
I have but not at my job
and the reason for that is that usually
the reason why you think you need these is because your
applying to startups
and startups they don't have enough resources to
hire the three arc types of Data Scientist
so Mapreduce, Hadoop and all of these things
those are usually the Data Engineers that work on this
or Software Engineers
cuz technically you don't need to know much about
data or statistics
to create these pipelines
so the Reality vs Expectations is that
you don't need to know these things
when you thought you did
yup
so for example
working at facebook especially because it's so big
they have three separate jobs for that
you know they have the Data Science Analytics
the Data Engineers and the Data Science Core
so Data Engineers would do all that
and you wouldn't even need to think about it
you can just focus on you know impact
and thinking about how to
you know
how to make a product better with the Product Managers
if someone wanna to get to Data Science today
what website should they go to to learn more
I personally don't use any websites
and I wouldn't recommend websites
I think you should definitely just try to get an internship
and to be honest
this a little bit harder
but if you do have a technical background
like computer science it would be better
and if you still can't do it
maybe try to get a consulting job
and then move into Data Science
okay, what's the last card?
the better you are at statistics
the better Data Scientist you'll be
yeah
so what I mean by better at statistics
we usually think about complicated models
advance forecasting techniques and stuff like that
I just wanna to tell you a little bit
about what happen to my internship
we had
five interns
one of them did some hardcore forecasting thing
that's like really complicated
but the only thing it forcasted was for example
the number of active users
for that specific product and maybe it was very accurate
but what is that give us for product
what kind of like product recommendations does it give us
it doesn't really give us anything
so in the end it's not about how good your stats is
or how technical you are
value can you add to the company
and that's what matters the most
because if you do many complicated things
and like a lot of machine learning stuff
but in the end your not giving any value to the company
even if it's so cool even if it's like
like really
advance stuff it doesn't matter
cuz I can do the same thing
with a simple logistic regression
or like a linear regression
as long as it has impact to the company
then that's what your valued at
it's interesting you are saying that
it's almost like working as a
as a developer too
it's related that you can develop this complicated code
but if it hasn't have any functionality
then there's no point
like it's kinda like the difference between like
doing something theoretical
versus doing something practical
theoretical can get so complicated but we can't use it
then what's the point
right exactly so I mean
a lot more then often
I see people over Engineer softwares
and then yeah it's really good and it's marginalized
but nobody can touch it
because they just don't understand how to use it
and that's useless
alright Joma last question
if you could go back to the beginning of your career
your freshmen you just graduated from highschool
would you do Data Science all over again?
that's a little bit of a hard question because
I do enjoy Data Science now
but I think there are somethings that I would like to do more
I always wanted to be a Product Manager
rather than a Data Scientist
unfortunately like through
this schooling that I had done
I didn't develop the skills as a Product Manager
I develop the skill as a Data Science or as a Software Engineer
so if I had to redo it
I probobly would have focus more on like
the business side of things
what did you see in your professional career were
now you would prefer to be a 
Product Manager than a Data Scientist
yeah so I saw that Product Managers
they focus more on execution
and they also get
more of the credit when things go well
in terms of different products
like in some sense a Data Scientist
is like the right hand man of a Product Manager
a Product Manager is like a mini CEO
so I always love thinking about products
and thinking about you know
new and innovative way to think about
you know how to reach users
and how to make their lives better
but usually it's the PMs that have the final say
I get paid a hundred thousand base salary
like I'm hired as a new grad
I get paid the minimum
I get a hundred twenty thousand equity
for four years that means like thirty thousand equity per year
and then for the first year you get
thirty thousand dollar bonus for the first year
and then you'll also get some random relocation bonus
that's worth like fifteen thousand or something like that
I hope you enjoy that interview with Joma
if Data Science sounds like a profession
you might wanna start learning about
consider taking this course I link in the description
on Skillshare
I'm your instructor Frank Kane
and I spent over nine years at Amazon.com
and IMDB.com
developing and managing some of their most famous features
like recommended for you and
I think it's a great introductory course
and with Skillshare you get access to
over eighteen thousand courses
for fifteen dollars a month
what a great deal
and with that being said I'll see you guys next week bye
