Hey congrats! You got the job. You rolled a Nat 20 on the interview check,
and now you are sitting pretty with a fresh FAANG data science job.
But is expectation going to match to reality? Stay tuned until the end to find out.
My name is Andrew i'm a data scientist here in silicon valley coach and a mentor and a youtuber
as it turns out that is professor meatball he's a doctorate from corgell i have a feeling you're
gonna want to listen to what i have to say until the end because i'm gonna cover the top three
biggest changes from reality to expectation i'll be covering frequently asked questions like what
are the hours like what is the work life balance how do i smash the like button for the youtube
algorithm and hit that subscribe button if i'm new and without further ado let's get going when you
think about data science from the ground up it is a very unique field that combines statistics
machine learning modeling experimentation python sql and a lot of different elements that i don't
even have the time to list although i could do a 10-minute long video where i just list exactly
what goes into a data science crock pot however the very first expectation versus reality mismatch
is the fact that you might think you might expect that you're coming to a fan company and start
making waves automatically you might think that engineers have direct relationships with you that
ask for your advice your statistical know-how for every single a b test or any product that they're
trying to engineer and any feature they're trying to roll out that is not what exactly happens the
first thing you'll learn is that data scientists are often siloed into different engineering teams
that means one data scientist could be serving an entire 20 person engineering team or 30 person 50
person 100 person team that kind of is a crazy ratio one person to 100 people who need to bend
that person's ear and make sure that everything in the company is running smoothly surprisingly
this comes with its own mismatch of pros and cons the pro here is that you are essentially someone
who fixes the fire engine when a fire comes out you're going to be able to help the fire
engine run more efficiently you're going to help future fires get detected faster but you are not
on the ground floor when a fire is actually happening data scientist is rarely called in at
9 00 p.m you know 12 a.m 2 a.m to solve a life or death engineering issue so that first big mismatch
of exactly how much agency you have in the company comes from the idea that data scientists are
always assumed to be thought leaders assumed to have the statistical rigor to drive decisions but
oftentimes it is the reverse in companies that either have built new data science teams or data
science teams are kind of in their infancy data scientists are essentially data moms or data maids
you're taking a lot more of a reactionary stance when it comes to data solutions and problems when
there is a problem to be had like a leak to be sprung you have to patch it instead of trying to
look for leaks to begin with prevention detection and streamlining come at a more advanced data
science team something akin to a 15 to 20 person team all with its own hierarchy and specific
documents and goals in mind if your data science team has vision maybe there's a director of data
science maybe there's someone who is actually calling the shot and trying to get you through
a sprint just like how a product manager could try to help an engineering team get through a sprint
then you would be in better shape to start driving agency then there's the caveat of your own word
being diluted in a sea of other statisticians and data scientists come with the expectation that
you will be an expert in the room but you might be one expert out of many or if you are a minority in
a larger team you might end up having your words fall in deaf ears number two is important to note
because this is something i already briefly talked about in the beginning of this video which is work
life balance you're probably expecting to work a lot of hours in fact engineers end up working
a ton of hours depending on how important their specific sprint is and how what their role is the
expectation mismatch is between data scientists and engineers engineers end ups working a ton of
hours because they are once again like the analogy of firefighters they're the ones on the ground
floor fixing problems and building new solutions they are the ones that are ending up working 50
if not 80 hour work week now that's a big stretch but we want to have a 95 confidence interval here
on the other hand if your data science team has well planned out work life balance you might end
up working only what is necessary working in day by day sprint by sprint progress this involves
creating and testing solutions at your own pace which allows data science to be a lot more lenient
on the work life balance probably ending up somewhere closer to 30 to 40 hours of work
week if even that this means more time with your dog more time to roll these metal dice that might
hurt if they fall in your hand really hard and more time to create educational videos and create
mentorships and other ways to fulfill your life this mismatch comes as a surprise because this
is a pro in fact it comes at a big competitive technical field when people are making six figure
entry-level salaries but also double that when it comes to stock options and yet you're not even
working as hard as your run-of-the-mill consultant having to fly all over the world and having to
answer questions to different stakeholders every single month no no no you're gonna be working for
a solid team someone who you are going to build relationships with and you're going to be able
to understand a product very well so that you can move up the ladder for any kind of specialization
you want to do in the future for that i would say work life balance gets a big a plus for data
scientists now a caveat is that not always the case for every single data science specialization
this is just a general understanding our third and final expectation and reality mismatch is how much
actual modeling and cutting edge technology are you actually working with if you're an entry-level
data science position in a fang company especially you're probably going to be kissing goodbye to all
of that theoretical modeling and mathematics that you learned in college this is likely not going
to be your day-to-day if anything it's going to be something you gut check every once in a while
maybe a month at a time whether or not you're tuning the right hyper parameters and having to
read a white paper to make sure that you're doing the right thing if you are an entry-level position
you're likely going to have to leave that to more senior positions people who look at your code and
approve it but that said a lot of your time is going to be spent individual contributions
you're going to be writing sql code for the most part if you're working in a company that
has fewer data engineers and if you're more of a data stakeholder business in between person then
you're going to end up doing a lot of excel work so if you're hoping to do some theoretical python
modeling building out different hyper parameters tuning them grid search cv then you probably
want to aim for a position like a research scientist or perhaps a machine learning engineer
those are going to be a little more competitive especially since they're looking especially for
someone with an engineering background or a very technical academic background but if you wanted
that kind of lifestyle a data scientist might just end up not being technical enough for your
liking wait just a second i'm actually not quite done yet because i have a special announcement
i'm starting my own hashtag thanks inspired to 66 days of data by kenji i'm going to start 60
seconds to data science which means i'm going to start as you might have guessed from yesterday's
video i'm going to start 60 second shorts for how to learn sql python and other important data
science interview questions that people seem to get wrong even after hours weeks months of
boot camp and entire master's degrees and phds for some reason people just don't teach that
stuff in school anymore this is inspired by my comment that anybody can learn sql in 24 hours so
eventually with enough 60 seconds we'll build up to 24 hours one day by 2035 i suppose and
then you can watch the entire playlist and understand exactly what goes into a data science
job furthermore furthermore i want to introduce someone special wait no this is not how i wanted
finally i'm going to explain all of these concepts to someone who
is completely non-technical who is not a data scientist
my girlfriend by the end of all of these videos you will be able to understand data science from
a to z and i hope to join you there so that's it this is a quickie i wanted to make sure you guys
know that i am trying to create some new content on tick tock over on tick tock i'm creating 15
second videos maybe i'll start increasing their length but it really is just me picking up my
phone saying exactly what comes to mind and making sure that the information that i put out there
is as juicy and packed full of facts and figures and useful hacks as i possibly can if you're
new here once again i would really appreciate you following me on a couple places including
right down below as a subscriber for my channel but i'm also very active on instagram if you want
to follow me there every single tag is the same and if you want to know a little bit more about
how a mentor works and how a mentorship is set up then i recommend you coming down to
the free discord where you can contact me directly and talk to other peers
exactly like you let's throw up that list of patrons who help support my transition
from data scientist to full-time data science educator today i want to shout out goliath59nph
this person is constantly giving great energy into the discord server giving great energy
into the comment section of my videos if you want to be shouted out go ahead and head to
patreon i take all the comments on my videos my community posts and my instagram put them
in a random number generator and i'm starting to pick one person a week and ultimately that's
gonna end up being one person a month or maybe even one person every couple of months so if you
want that you know what to do as always for now but not for forever peace talk to you guys later