
Data Science Is America’s Hottest Job - dsgerard
https://www.bloomberg.com/news/articles/2018-05-18/-sexiest-job-ignites-talent-wars-as-demand-for-data-geeks-soars
======
kitanata
I recently quit a gig and spent a few months looking for a data
science/machine learning gig. I was surprised just how gatekeeped these
positions were. Everyone wanted a PhD or a masters degree. I have 15 years of
experience in software. I’ve done everything to low level game programming and
graphics programming to web development, to AI (but not as a specific position
title).

The gatekeeping in this field surprised me because in my study of data science
and machine learning I did not think the practical use of these techniques was
that hard. The math isn’t even that hard if you had to implement these
algorithms from scratch. It’s just linear algerbra and calculus, which anyone
with an engineering degree is going to at least have exposure to. I couldn’t
get the time of day from anyone. Not even a call back to prove that I knew or
could learn what was needed to be effective. It was incredibly frustrating and
disappointing.

Data science / machine learning is not that hard, and you are turning away
good candidates for bullshit reasons. Stop it. At least bring them in and talk
to them. Jesus.

~~~
hellofunk
Of the skills necessary to succeed in data science, the ability to program is
actually the least important. Perhaps you were over-emphasising your software
skills at the expense of really demonstrating proper understanding of the
_science_ of data analysis. A data science position is not going to require
your experience in low-level game programming, for example. In fact, being
able to program at all is secondary, since many can pick up some basic skills
in languages like Octave, R or even Python, to support their mastery of the
math. And it's also not so much math implementation but also a genuine
scientist's eye on how to approach a problem. There are no cookie-cutter
formulas you can throw at a non-trivial problem: you have to really understand
the field and the art of analysis to do it well.

~~~
kitanata
This right here is a perfect example of why this position is gatekeeped so
much. Having a PhD doesn’t automatically mean you think like a scientist of a
mathematician, and having a bachelor’s degree and 15 years of experience
writing code and then putting in the effort to read Elements of Statistical
Learning, compeleting several online courses in machine learning and data
science, studying probability, combinatorics, graph theory, does not mean you
don’t have a scientific mindset. A scientific mindset can be learned. You are
not special because you have a PhD and I don’t. The only difference between
you and me is that I had the ability to learn for free what you paid for. But
when you go “oh hah, he’s just a coder and only has a bachelors degree” and
you won’t even call me to talk to me that means you are missing out, and you
are gatekeeping. You are not special and you are not smarter than me. You’ve
just read a book I haven’t and wrote a white paper. I can read that book too.
I can write a paper too.

I can do everything you can do.

~~~
nycthbris
I think you are seriously underestimating the value of first hand experience
conducting scientific research. It beats into you a mindset you can't get
anywhere else. There are no books or manuals or docs to read and master it.
There's only the scientific method. You start with a question, conduct
experiments, analyze results, adjust your hypothesis, and repeat the process.
You get very comfortable with saying "I don't know". You right at the border
of the unknown and trying to navigate further.

Companies are using a PhD as a proxy for having research experience because
it's the the only qualification like it out there. It's a poor proxy because
not all PhDs are created equal.

~~~
peatmoss
> You start with a question, conduct experiments, analyze results, adjust your
> hypothesis, and repeat the process.

This is missing my pet step: doing the literature review.

I’m pretty ambidextrous when it comes to Python and R, so I’m not typically a
combatant in the data science language flamewars.

But... for as much as the Python community likes to assert their superior
coding chops, I’ve observed that the R community does a much better job of
reading about prior art.

~~~
notlob
> This is missing my pet step: doing the literature review.

One of the earliest, most important, and most useful lessons I learned from a
senior grad student: "a day in the library can be worth a week at the bench."

~~~
mathinpens
Or this: you spend 4 months on a project and belatedly notice schmidhuber
proved a much better result in 1985 and yells at you and you get kicked out of
academia

~~~
newen
You don't get kicked out of academia for that... Well maybe if you're a
student and you lose your funding. It's really embarrassing and I'm sure
frustrating too. I've reviewed a couple of papers where they didn't do their
literature review, there were lots of obvious (to people who read) prior work,
and I've had to straight reject their papers. I feel bad for them but do your
literature review folks!

~~~
mathinpens
I was kidding!!! But people do take literature review seriously (which on the
balance is good)

------
wakkaflokka
I'll be completely honest, as your standard "data scientist" who hopped on the
bandwagon and came from having a PhD in academia in an unrelated field, I
cringe at these articles. I'm not entirely sure why. I think it may be two-
fold:

1\. A little bit of the selfish "oh no, the secret's out, at what point is my
salary going to drop when the demand is met by the dedicated Master's degrees
and bootcamps?"

and

2\. These articles seem so incredibly corny, it's almost embarrassing. The
"hottest job"? Ahhhh, stop it. But these things go in an out of phase, similar
to back in the day when "anesthesiologist assistants" (CRNAs, AAs) were the
hottest thing for Bloomberg to talk about. It will not last forever.

The irony is that I probably only knew "data science" (always in quotes)
existed because I read one of these cheesy articles. I mean, we all know that
statistics have been around forever, but that there were dedicated positions
where you could run stats, build models, and then deploy them all in a single
role was foreign to me.

So it's a combination of a potentially irrational fear of self-preservation,
and laughing at the state of affairs where some basic stats work will pull in
that kind of money.

I tend to have fears about the future, always wanting to hedge myself so I
don't become outdated. In the data science sense, I see the field becoming
super super broad and eventually saturated with new supply, so I debate on
whether I should pivot into management of analytics in general or not. I.E.
getting my hands off the keyboard. Ultimate goal would be to help define,
strategically, how statistics/data mining/machine learning/yada/yada/yada are
used at a company.

~~~
baxtr
Just add a grain of AI or blockchain and you’ll be fine!

~~~
frockington
Do a linear regression, call it ML with AI and you'll be running your own team
in a week

~~~
sannee
Just the other day, I heard someone talking about a "single layer neural
network with no activation"...

~~~
retbull
y=mx+b :D I won

------
pmcollins
My wife is a data scientist and this is one of her favorite quotes, from Dan
Ariely:

Big data is like teenage sex: everyone talks about it, nobody really knows how
to do it, everyone thinks everyone else is doing it, so everyone claims they
are doing it...

[https://www.facebook.com/dan.ariely/posts/904383595868](https://www.facebook.com/dan.ariely/posts/904383595868)

~~~
nabla9
"Deep learning is like big data: everyone talks about it, nobody really knows
how to do it, everyone thinks everyone else is doing it, so everyone claims
they are doing it..."

Tim Hopper
[https://twitter.com/tdhopper/status/916383020835368960](https://twitter.com/tdhopper/status/916383020835368960)

------
ryguytilidie
Recruiter here. Data Science is one of ~3 roles I generally don't recruit for.
Not because Data Scientists are super hard to find, but more because companies
seem to all want data scientists and all want them to do completely different
things and none of them really understand those things. Makes for a pretty
lame hiring experience unfortunately. :(

~~~
Mc91
I know a mid-level manager at a Fortune 100 company. Somewhere up the
management chain, someone read an article about how data science is the hot
new thing that your company has to be doing or it will have missed the boat.
So word was sent down that their next project was to do data science. So now
they are launching a data science project to analyze their business data,
which this manager is overseeing. They're hiring data scientists and all that
entails. It doesn't come from a business requirement or some organically
generated need, top management just wants to be able to say their company is
using data science if they're asked about it.

~~~
tormeh
That's unfair. "Not missing the boat" is a natural need for a company.
Everyone with an MBA has studied companies that missed the boat. Throwing some
money at various new fads isn't necessarily irrational.

~~~
ItsMe000001
Nevertheless, it _does_ make one reconsider million dollar compensation
packages if all those guys can come up with is to deploy randomness. Either
they have foresight and a plan or they don't. Either way is fine, but only the
first option supports the million dollar salaries for executives. If they are
just like any other mortal... they didn't even do anything to identify new
trends, they just followed what was in the major business magazines.

------
peatmoss
I have a love / hate relationship with bucketing a whole lot of formerly
separate disciplines into the data science label.

In my case, I'm a reasonably solid R / Python programmer (who occasionally
dabbles in racket, clojure, and others). I've got the sort of applied
statistical training of someone who took a quant-heavy course load in a PhD
program. I've even (by title) been a data scientist a couple times now.

Having recently decided to reenter the job market, I'm reminded that finding
the RIGHT data science role is a major challenge. When someone wants a data
scientist, they may be looking for someone with a lot of specialist depth in
operations research, financial forecasting, machine learning, data-focused
software engineering, or some other not-at-all-universal area of expertise.

In some ways, I almost think that someone with a bootcamp level understanding
of stats may be at an advantage. Whereas I'm very inclined to be, "Oh! You
want this other kind of person. Would you like me to put you in touch with
one?" I think someone more junior is inclined to be "How hard can [X] be?"

~~~
posix_compliant
As a "junior" applying to data science positions, I have a 50/50 mix of "no
way I'm qualified for that", and "that sounds like a really fun problem."

~~~
peatmoss
Welcome to impostor syndrome. It NEVER goes away. The worst part of impostor
syndrome is that sometimes, you really aren't qualified, and so you can never
fully move past the self-doubt.

You don't sound like the Dunning-Kruger sort, so I'd chase the fun sounding
problems in organizations where you can use more senior people that you
respect as sounding boards / mentors.

Good luck (to us all)!

------
tom_b
FTA (examples of data science):

    
    
      targeting health-care customers for hospitals 
      people who can turn social-media clicks and user-posted 
        photos into monetizable binary code is among the biggest 
        challenges facing U.S. industry
      “sentiment analysis,” or finding a way to quantify how
        many tweets are trashing your company or praising it
      determine how customers prioritized paying bills
      “recommendation engines” - those programs that predict
        what you may want to buy next
      advertising
    

My background is traditional business intelligence, finding actionable data
for high level leaders.

A common response on this thread is that little data science is actually
occurring in the business world. It would be incredibly useful to me (and I
suspect other readers of HN), to hear from other participants on the thread
what data science and methods they are using.

I'll kick it off with data analysis examples from my workplace:

    
    
      1 - Analysis of patient accruals to various clinical trials.  
        Mainly tracking against goal numbers.  No statistics or ML.
      2 - Analysis of tissue collection opportunities to answer the question:  
        Are we obtaining samples useful to future research opportunities?  No statistics or ML.
      3 - Creating models to accurately predict patient accrual rates for individual studies across various 
        different variables (race, ethnicity, gender, age).
        Simple statistics, probably just a linear regression model.  This is a new effort.
      4 - a long, on-going, and currently unsuccessful attempt to extract useful data from pathology 
        reports (free text descriptions from pathologists examining various collected tissues for 
        both medical treatment and research purposes)
    

In addition, I know of a few NLP motivated efforts to train classifiers - say,
given a set of 500 manually labeled papers, can a classifier be built that
would be effective in bucketing an additional 8,000 papers?

------
brookhaven_dude
We have two closely related groups in our company. Mathematical Optimization
and Data Science. I am in the optimization group, but when I query the data
scientists, they seem to be doing similar things - simulation, network
optimization, resource allocation and planning. I am not sure why management
wants to call them data science.

~~~
ims
I'd guess that historically the Mathematical Optimization department has
comprised "legacy" operations research people (spreadsheet modeling and
decision analysis, linear and nonlinear modeling with AMPL-type tools, SAS
statistics, etc) and the Data Science group is newer and more software-ish.

Am I right?

~~~
brookhaven_dude
I guess so. They work with SAS and python, while we work with C++ and CPLEX on
vi editor in a linux terminal.

------
minimaxir
Recently I wrote a tweetstorm of my not-so-great experience hunting for a data
science job last summer:
[https://twitter.com/minimaxir/status/951117788835278848](https://twitter.com/minimaxir/status/951117788835278848)

Despite data science being a hot job, the sheer, growing number of MOOCs
available will cause the high amount gatekeeping from many prospective
employers to get even worse.

I am _very_ happy as a data scientist now, and yes, it's more complicated than
doing Excel VLOOKUPs! Although I did get rejected from those VLOOKUP positions
many times in my job search...

~~~
gaius
_sheer, growing number of MOOCs available will cause the high amount
gatekeeping from many prospective employers_

I did a MOOC, nothing wrong with them. A lot of it turned out to be just
revising things I had originally learned in my BEng or MSc, but I learned some
things too. And a bit of revision never hurt. I don’t think I had done much
calculus “in anger” since graduating for example, so that was rusty.

In 5 years or 10 years there will be no data scientists as a full time job -
the skills will just be folded into regular programming jobs or accounting
jobs or whatever. If you’re in that job now, make hay while the sun shines

~~~
mikert5671
"In ten years all programming will be either outsourced or automated..."

-everyone in the 90s

~~~
gaius
When I started in this game, just being able to put up a website made you hot
stuff. HTML and CGI were cutting edge. But for a long time now, those skills
are totally commoditized. That’s what I mean.

------
tomrod
Interesting. Like others point out in then article, I do field a lot of
recruiters as an economist/data scientist, but most of the initial offer
discussions have silly attributed.Base range max lower than current base
salary, contract-to-hire, etc. Having been doing this type of work for almost
a decade now, I think this kind of article comes across as recruiter
uncertainty rather than identifying the real value add of data science:
extracting valid insights from data.

If you want data scientists, pay a good salary and good equity.

------
calvinbhai
Looks like this article is well timed for the first batches of MS in Data
Science grads who'll start looking for jobs soon. I'm not a data scientist,
but I feel Data science field is still quite vague, and the terms ML, AI, Deep
Learning etc are thrown around so much, its hard to understand or make a guess
as to how much real demand is there for such jobs.

------
geogra4
One of the most ill-defined too.

~~~
kurthr
I'm actually not that impressed at $160k for "the hotest job" in a major
metro, and it sounds like their example with a masters in Stat might be using
something more than Xcel plots.

Kind of disgusted though, that Equifax "is shortening the hiring process to
keep anyone from slipping away." They could fix their security practices
before hiring anyone with Scientist on their resume.

~~~
freehunter
I’m sure the people implementing their security are the same people recruiting
and hiring new employees in an unrelated field.

------
MadSudaca
I don't think data science would be a hot job if advances in data engineering
(arguably, another ill-defined term) hadn't been achieved. Because of this I
like this definition of data scientist, although I can't remember where I read
it:

"A data scientist is someone who knows more about statistics than your average
software developer, but more about software development than your average
statistician".

~~~
190807
Or: "A data scientist is someone who knows less about statistics than your
average statistician, and less about software development than your average
software developer".

~~~
MadSudaca
Also valid.

------
coldtea
A more correct version would be:

"Is America's current fad job, like tons of similar jobs before it" (have
lived through 4-5 of those "hot jobs").

~~~
solomatov
What were the fad jobs in USA before?

~~~
coldtea
E.g. around the dot-com bubble it was "web designers" \-- now they're a dime a
dozen.

In the 90s/early 00s there was the biology/bio-engineering/bionformatics trend
-- everybody was thinking of going into biology when I went to university --
that dried up soon as well [1]

[1]
[https://iubmb.onlinelibrary.wiley.com/doi/pdf/10.1002/bmb.20...](https://iubmb.onlinelibrary.wiley.com/doi/pdf/10.1002/bmb.2005.494033012428)

Here's a good list written by another HN member:

[https://news.ycombinator.com/item?id=8864737](https://news.ycombinator.com/item?id=8864737)

------
wuliwong
I've had a surprising experience with hiring data scientists in the last year.
I have hired two (one full-time and one part-time) and am in the process of
hiring a third. During this time I have also been looking for a SugarCRM or
SuiteCRM developer.

I have been flooded with candidates for the data science position. There seems
to be a glut of well qualified [1] data scientist applicants. I have turned
down many candidates who I think would also have done well in the position.
The CRM developer position has been extremely difficult to fill. This has had
the effect of bringing the salary of those two positions in our company to
near equivalence. My initial expectations for the salaries were that the data
scientist would be paid substantially more than the CRM developer. I know that
Sugar/SuiteCRM aren't super popular but it also could be that I have been
effected by all the attention that ML and AI have gotten over recent years.

[1] well qualified for us has generally meant masters level or above with real
work experience. maybe this definition is the source of the 'glut' of
applicants?

~~~
mr_toad
I’ll be blunt - working with data is a lot more fun than working with line of
business applications. That’s why so many more people want to do it.

People compete in machine learning for fun (e.g. kaggle.com). I doubt anyone
works on CRM systems for fun.

------
mercwear
Unfortunately a lot of the "Data Scientist" I meet are nothing more than excel
/ sheets gurus. Most of them have never written a line of code or syntax that
is more complex than a nested vlookup.

I hope the tide changes here and I think it will, but this has been my
experience so far.

~~~
dx034
I had a feeling that in Europe, a lot of those jobs run under Data Analyst.
Data Scientist roles at small companies can go in that direction but most that
I've interacted with strictly don't do Excel anymore where possible. Mostly
because they found out that turnover is very high if you hire Data Scientists
for VLOOKUPS, so you'd better give talented people other tasks to keep them.

~~~
frockington
If I were European i'd scope up one of those boring jobs and then do another
job while at work. Those job protection laws (at least in France) are free
money if you work it right

~~~
dx034
If it makes you happy, sure. Most people I know prefer an interesting job they
like, rather than 8 hours of boredom. Remember, the employer can very well
deny you any personal internet access. Personal internet usage at work is
grounds for dismissal even with strict labour laws.

~~~
frockington
Interesting take, I never thought of that

------
monadmancer
The White Walkers of the data science field are out the box enterprise
solutions. These are enterprise software, data science consulting and ops
solutions in a package. The corporate customer need not hold in house a data
science team. The ambitions of the enterprise solutions (think DataRobot, H2o,
etc) are to effectively bring one click production ready solutions that even
C-level can participate in.

I see this is as the greatest threat to the demand of the "in house" data
scientist.

If this turns out to be the case, I see the greatest demand for those who can
write production grade code (i.e. software engineers) and those who are
effectively trained data scientists. We see this job often called research
scientist or research engineer.

------
darkhorn
Employers rediscovering statistics.

~~~
tomrod
Bingo.

------
wiradikusuma
It is hot in this part of the world as well. I have a few friends who wouldn't
pass as programmer (not because they don't know frameworks, but because
they're weak in logic), but earn handsomely when they claim as "data science"
guy.

------
rufius
Data Janitoring is America’s Hottest Job __

FTFY.

~~~
deviationblue
Ill-defined though it may be, there's still an understandable difference
between data science and data engineering.

------
clatan
You'll also need a strong background in statistics if you wanna bring value to
a company other than "machine learn all the things".

I'd even argue that - for data science - it's more important than any ammout
of years doing engineering.

------
madengr
My wife will be attending a university based data science boot camp. She has
spent 12 years as s microelectronics process engineer. Seems a dead end job
and can’t break the $100k mark. Hopefully it will be worth it.

~~~
zpatel
But is that something she will like ?

~~~
madengr
Yes, as she already does lots of statistical process control, design of
experiments, etc.

------
mistrial9
at a certain company in San Francisco, they had Data Science meetups very
publicly, for a year or more.. very well attended.. The company made an
announcement they were 'hiring' at many of them ..

It appears to me that about two or three people were hired that way over more
than a year.. at the same time, the company was being sold to a larger company
in Seattle.. the founders made at least seven figures in the sale, I would
guess... draw your own conclusions..

------
brookhaven_dude
Where are these $300k salaries? I have a PhD and work in Atlanta. I never got
the memo on these sky high salaries. Don't know anyone else either who earn
this much.

------
arca_vorago
I'm currently pursuing my data science degree after my last burnout as a
senior sysadmin. I'm hoping my practical experience will be a perfect
augmentation of the "data science" stuff so that I can be more confident when
bringing the board or the execs proposals about how to fix things.

Let me tell ya'll all a little secret. Execs have been mismanaging
infrastructure... and it's all so close to crumbling at the first puf-o-wind.

------
mixmastamyk
Definitely could use a high paying gig, but crunching surveillance data
without opt-in has always felt unsavory and wrong to me. Their examples
including Equifax made me cringe. Any perspectives that might help me feel
better about doing it?

------
amelius
After the Facebook data leaks and such, I suppose the field got a bad rep. So
not sure if such jobs are really hot. Perhaps the author is trying to
influence public perpeption?

------
deusofnull
What about Firefighters???

