Long time no bark, humans!
My name is Professor Meatball
PhDog
and I am talking about how to spot a fake data scientist today
I put a surprise at the end of the video so I'll see you there
Awooooooo~...!
[Snazzy hecking intro]
How's it going welcome back to DataLeap
We are bringing data science down to earth
My name is Andrew and we are going to talk about
our favorite meme lord saltmaster king of the drama data scientist
John Singer electrical engineer data scientist
Man
after
my own heart
[Flashback] I am actually about to get fired after six months on the job
That's sad
I just passed the six month mark and i still have not been fired
oooooooh
final update i have officially been let go
now with the coronavirus nobody's hiring
HELP!
what a roller coaster
he answers a question
and the question today is: Can a data scientist fake it
until they make it
He gives his personal take on whether or not a data
scientist can fake it
[John] Unfortunately i think you can
but only in an unsuspecting organization that doesn't know any better
a fake data scientist is what we call a
black box button pusher (BBBP)
Okay it's a lot of words that is
they can instantiate pre-made library functions but have no clue
what goes on under the hood in fact i have a fake data scientist in my team
who was hired before i came and i knew she was fake from day one
this man
is the master of salt this man is very good at creating this kind of drug
in his own team his full name is here unless that's not his real name
but if it is his real name is the fact that he was able to talk about
his own team like this um she can answer all the basic questions
like supervised learning versus unsupervised learning that'll fool
most people but she didn't fool me john singer
master of
data fake data scientists are a big issue these days if you've heard of a
company data robot they claim one of the big
reasons they came into existence is because there were too many fake data
scientists hidden in an organization this is true but this also strikes me
more as a marketing point for their business
i do not work for data robot that's a perfect ploy for someone who does
and i am not endorsing their product i just like their philosophy on fake data
scientists besides i hear they have a huge toxic
culture with a high turnover but then again that
sounds like every other tech company on glass door
lol our boy john back with the lulls it's he just not only called out his
team member someone who can be verifiably
like from his company he also calls out this whole other company and says
they're toxic and then he calls out every single tech company on glassdoor
to be a man with so few flips to give oh see the
field in which i sew my flips and see how barren it is
you gotta love john wow he makes good content he should be a youtuber i want
to subscribe to him by the way subscribe like if you like
this if you want to see more john this could just be a john channel
companies don't know how to hire real data scientists consequently they hire
some good ones and some fake ones this is true in the sense that companies
who hire data scientists and i'm going to cover this an entirely
separate video but they're often looking for
someone who is either a machine learning engineer or they're looking for someone
who's a data analyst data scientists perform both of these
tasks but they're not necessarily someone who
lends their skills in in creating this task
it's been prevalent through the entire industry before data scientists they
were uh you know business intelligence uh
consultants uh this conflates the two positions that almost have nothing to do
with each other a data analyst munches through data uh picks and
chooses what they want to see and then maybe puts it through excel and
presents it on a powerpoint they need to have the interpersonal skills
in order to make sense to every other data stakeholder
so the designer the product manager the sales person they all have to understand
what the data analyst is saying so therefore there's a lot of
interpersonal communication and cross-functional team support
but a data scientist they work with data in a more theoretical
and model based sense they'll pump data either through
a very high functioning algorithm but in the higher end of the spectrum
they'll be working on machine learning models and artificial intelligence this
is a blanket term for the kinds of deep learning models
that exist in industry so when amazon recommends hit movie or
netflix knows which exactly what show you'll be dying to see when it comes out
that's because a data scientist a machine learning engineer was able to
plug that in in the back end that person doesn't necessarily have to
make pretty graphs that's an entirely different job it's like
combining a baker and a brick layer a bricklayer can build
an oven then the baker uses the oven so i guess
they're kind of related but you see that if you have or don't
have the infrastructure already in place you truly only need one or the other
let's keep going the norm or trending norm today is to
have data scientists in every department such as marketing
hr et cetera so what ends up happening is
there are many fake data scientists hidden in each department
sure john's coming to the point that like
data scientists work in silos in a fan company each data scientist
actually might not work together on sharing different resources
on collaborating on specific projects on recreating code or removing the need
for superfluous code they'll actually work
depending on what what team they're on so an hr data scientist might actually
never talk to a marketing data scientist depending on how big the team
is depending on how big the company is so that's problematic
because uh one you're wasting a lot of potential because
models and product senses uh flow very well throughout a company
right so a data scientist is always working
with half of the picture they're not working with this
the whole picture the other problem with this is that with this
divide of information john is saying that
suddenly you don't have the ability to understand
how many fake data scientists there are right if there was a big head count
every single year and we said okay these data scientists
measure up here and these data science measure up here
that would be easier but therefore it's harder since there's so many different
siloed data scientist roles fake data scientists build bad models which is
actually worse than having no data scientists
because they build flawed models that do more harm than good
after all when a data scientist builds a model most non-data science staff
do not question the validity of the model hardly anybody pauses to ask
is this person doing the right thing is he qualified to do this what qualifies
him i have to say that the problem with this
argument is that when you go to a dentist or a
doctor and say show me what qualifies you what
they can show you the piece of paper right they can show you that they
they had the degree they had the residency uh
experience compounds and layers on expertise and professionalism
um so it's not easy to see out in the open i would never go up to
someone and ask them on a team what qualifies to make design
right what qualifies you to make a scalable infrastructure as an engineer
what qualifies you to sell as a salesperson
but at the same time he is right that nobody
asks whether or not the model is correct this is
integral right you need to test test test test engineers don't like to do it
data scientists don't like to do it but if you don't have the tests to back
up your model that will do more harm than good and
that's true for both fake data scientists and
real data scientists i suppose what he's trying to say here is
it's not like you can catch a fake data scientist with a model
especially since human explainability goes out the window
for modern machine learning and the more abstracted from human understanding
uh the easier it is for a fake data scientist to get away
finally data robot uplift themselves caught a typo finally data robot uplifts
themselves by saying that the answer to this issue
is one of their products which of course is
automl automl is huge today to where data scientists might soon become less
desired because you have an online tool that
does all the math science and model picking for you john
come on man what are you doing giving out all the secrets such as how
much a data scientist makes over here on the top left corner it
knows what algorithm to pick and tune all you need to do is a feature
engineering part and feed in the right data
this really is the future automl cloud ml etc
but we are still not there yet because most companies need a data scientist to
take care of at least some of these things in-house this
reminds me of that simpsons quote where uh there's this general and he's talking
to a bunch of recruits and he says in the future all wars are going to be
fought with tiny robots but there will be
people to maintain and clean those robots
and those people will be you
this is true automl will reduce the necessity for data
science but like i cover in a couple of my videos
data science is still a big mismatch between demand and supply in the good
way data scientists are in heavy demand and until
ml becomes a lot more reliable and a lot more human explainable
it's not going to catch on as quickly as john is foretelling it will
but once it does catch on it actually still necessitates a good data scientist
so this just is a positive feedback loop
you want to be as good of a data scientist as possible
understand your statistics understand your calculus understand
your linear algebra and then when they need someone who can actually
oversee all of these auto ml automations you're that guy that's you you did it on
that last point you may not even need a real data scientist in the future
a data analyst or a fake data scientist could just
upload data to a cloud platform and run a few tests and get results
remember that the auto ml tools will pick the model optimize and give you the
results heck the date the technical manager
could do that i would love to know how to read
remember that the auto ml tools will pick the model optimize and give you the
results heck a technical manager could do that
at that point and even a marketing guy i worked in a company that tried to
abstract natural language models as easy to
work with as possible for a non-technical consumer
i have to say that a marketing person can do it
yes but a marketing person will ultimately become
a consultant right a solutions consultant as as the industry would like
to say that person ultimately becomes a
handholder and therefore they cannot scale right they're not the
person that actually ends up controlling all the automation
they're actually just the person that sells and provides support
for consumers that use this product all until that gets automated too but
we're not there yet to be fair there are about 50 companies
that have such auto ml tools only 50. john can name them all can you
to be fair john is very fair i would like
john singer for president but as of today
let's assume you still need data scientist in-house to run complex models
how do you know if the person you are hiring is good enough here is my
definitive list this should be its own video i feel like
i've had so much fun with john today um this is i'm
this should be his own video john is john is such a wow there's so much more
john
are you still there
to hear more options what are you talking about
you