hi friends welcome back to another video in today's video i'm going to simplify the data
science jaw family specifically the role that is titled data scientist generalist if you have
watched some of my previous videos you already know that the whole data science jaw family
specifically when it comes to definitions it's all messed up the titles do not match the job
descriptions and job descriptions do not match the titles and the problem gets even bigger when you
compare one role to a different company because a data scientist at company x does not mean the
same at company y i have been thinking about this problem and i wanted to share my solution
and how we can simplify the data scientist role furthers to make sure that there is no confusion
around what data scientist actually does and there is one company that is actually doing it well and
i will share that company name toward the end of the video and as well as share some of their job
descriptions that you can look at and see what exactly are they doing correct that makes it
easy to understand what are the expectations for a data scientist rule so with that let's jump into
the video and if you find the information that i share today make sure to give this video a thumbs
up and subscribe to my channel if you want to hear more about data science tech lifestyle
career tips and everything else in between all right so let's talk about the data scientist
generals generalist role what it is so look at this diagram so this diagram is basically the data
scientist generalist rule that we're talking about is basically the middle part where it's a mix of
engineering mix of statistics myths of machine learning mix of business understanding as well
as data analytics so this is basically the role is a central role which combines understanding of
all the areas and is serves more as a generalist where you are typically not really building going
into deep machine learning building models from scratch but rather using the existing
libraries existing models and applying it to solve the business problem however the problem with that
is that since this role covers everything it can get confusing real quick and it's very easy for
one company to take that role and turn it into move it into the left of the diagram
and move it to the right of the diagram or move it to the bottom of the diagram
so when we think about data science generalist role there are typically three areas that somebody
could be working on one is they can either be like building metrics they could be either looking at
business and trying to understand what metrics we are going to measure how to improve a certain
metric and things like that the second area that could be more around like hypothesis testing where
you're trying to run experiments and trying to do a lot of experimentation and trying to understand
what actually works well and what doesn't and then there is like a third area which is more around
the machine learning side of things where you are actually building models you are where you have a
very good understanding of mathematics and you are able to use that machine learning knowledge
and are able to solve business problem by applying those machine learning models so
these are the basically three areas that i see a data scientist generalist role focusing on
however the problem is that when you combine all those three role into one it can get confusing
pretty quickly and it can get intimidating pretty quickly because it's very hard for one person
one role to be very good at all of these three areas i have been in data science roles where
some of them are very analytics focused and other ones are very experimentation focused now
is it realistic to expect one person to be very good at all those three areas probably no so i
think what a good solution and how we can divide this jaw family further into a more simplistic
job descriptions that is easy to understand and replicate across company to company so
that would be turning this role into three roles so there would be a data scientist who focuses
primarily on analytics this is something similar to what facebook's current data scientist does
is primarily focus on analytics and a bit of hypothesis testing then there is the second
role which is data scientist inference and that is primarily focusing on like experimentation
and things like that in hypothesis testing and then we have a data scientist who is primarily
focused on machine learning and they are primarily working with business to solve the business
problems um i mean all three roles work with the business but this role specifically works on
solving problems using machine learning so data scientists analytics data scientists inference
data scientists machine learning so i promise to show you one company that has been doing this well
and the company that i am talking about is airbnb if you go to airbnb's career website and you look
for data scientists you will actually come across three different data scientist focus areas and
those three different focus areas would be exactly this and if you look at the job description the
first line of the job requirement it would be very clear to you what does data scientist analytic
does what does data scientist inference does and what does data scientist machine learning does i
give kudos to airbnb for coming up with this this is not something i came up with this is something
airbnb came up with and i got to learn about it while i was interviewing at airbnb and when you
are interviewing with them they actually give you the option and they ask you like okay what kind of
data science work do you want to do and based on that they will basically give you interview and
the interview for all those three would be very very different now in other companies what is
happening is that their data scientist is like a mumbo jumbo of everything so which leads to
interviews being very very different from company to company so i think if more companies adopt what
airbnb is doing with their data science job family i think we can solve this problem of
having a mumbo jumbo of data scientist role which is somewhat unrealistic and i didn't even touch
on data engineering part a lot of the companies expect you to do data engineering work as a data
scientist which is completely unrealistic i come from a data engineering background yes having the
data engineering concepts and did help me as a data scientist but it's it's i don't think
a data scientist should be spending their time doing the data engineering work so
that's all i wanted to share let me know in comments what are your thoughts on
this three pillars of data science role do you think that would solve the current problem that
we have in the industry where there is a lot of struggle when it comes to job search job
interviews as well as like day to day because it's very hard for people to understand and for you to
sometimes understand what you're supposed to do and what you are supposed to focus on so with that
thank you so much for watching i hope this was helpful and let me know in comments what are your
thoughts on this topic and i hope you're having a great day i'll see you in the next video bye