
Desperate for Data Scientists - charlysl
https://spectrum.ieee.org/view-from-the-valley/at-work/tech-careers/desperate-for-data-scientists
======
PeterStuer
Two remarks from an old AI guy here.

(1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that you
actually have 'work' for the highly coveted person you are trying to hire.
'Everyone else is doing it', 'all our clients have data, so selling them data-
sciency projects shoudn't be a problem', 'Hire a first DS person, to have them
start a brand new DS division' are things that probably are doomed to fail. If
you weren't selling classic Data Analyses/Intelligence services before,
chances are you are lacking the foundation in your sales and support staff,
and maybe even your customer type, to be able to embed this into your company
and into a new line of business. The risk your prize new employee will walk to
greener horizons to work with peers on exiting real projects is high.

(2) Know where you are heading. If you just want to embellish your 'normal'
projects with some gimmicks from the online 'Cognitive Services' api's offered
by the various vendors, then yes, sending some of your regular staff on a
quick 'boot-camp' style course will probably work. You don't need a 'data-
scientist' for that. And if you hire a real one for that type of work he'll
probably walk within 2 weeks.

~~~
apohn
>(1) If you're going to hop on the 'hot' AI/ML/DS bandwagon, make sure that
you actually have 'work' for the highly coveted person you are trying to hire.
'Everyone else is doing it', 'all our clients have data, so selling them data-
sciency projects shoudn't be a problem', 'Hire a first DS person, to have them
start a brand new DS division' are things that probably are doomed to fail.

I'd add that if you have all these conditions and still want some hope of
getting value from your Data Scientists, give your data scientist(s) a period
of time (6 - 12 months) where they can discover how they can provide business
value. Accept that during that period they may not provide the value you are
hoping for. Support them if they recommend changes (e.g. different ways of
collecting or processing data)

Lots of middle/upper managers want to hire a team of Data Scientists and want
them them to deliver high business value from day one. They want a magic black
box to solve their business problems. This results in exactly what you
describe - constant fires, games, gimmicks (that look good on PowerPoint), and
good people eventually quitting or getting fired.

On the other hand, if you give them 6-12 months to interact with different
teams and identify relevant business problems, they'll find ways to provide
value. Or, worst case scenario, they'll tell you (maybe by all quitting) that
your problems aren't data science problems and the money you are spending on
them is best invested elsewhere.

~~~
crispyambulance

        > if you give them 6-12 months to interact with different teams and identify relevant business problems...
    

I fully agree with your points BUT 6-12 months of self-directed, open-ended
discovery just ain't gonna happen. This isn't the era of Bell-Labs, this is
the era of PMP-certified project managers. In many places the "data scientist"
will be dropped into a project and expected to start producing "deliverables",
outcomes will be a mixed bag.

A more organic approach would be for teams to start picking up tools and
skills for basic data analysis. We used to call it "stats". Doesn't have to be
cutting-edge ML stuff to provide value and new opportunities for the
practitioners. Of course whatever they start doing, HR and management will
pump it up: mining a few hundred megs of csv files all of sudden will get
called "big data".

Something similar happened ~10 years ago with the advent of "BI" (Business
Intelligence). It was nothing more than folks realizing that their corporate
databases have useful stuff in them and they could query them in an ad-hoc
fashion _outside_ of the applications that typically use them to provide
"business value".

~~~
apohn
>A more organic approach would be for teams to start picking up tools and
skills for basic data analysis. We used to call it "stats". Doesn't have to be
cutting-edge ML stuff to provide value and new opportunities for the
practitioners. Of course whatever they start doing, HR and management will
pump it up: mining a few hundred megs of csv files all of sudden will get
called "big data".

Here's my one caveat to this based on personal experience. This happens to be
why an entire data science team in one division of a large company quit over
the course of a year.

If middle/upper management is purely focused on "show value on day one", it's
very easy to get stuck in a position where basic stats and building Dashboards
for simple data mining becomes the gold standard for what a Data Science team
is for. Management develops an addiction to fast data mining projects that
look good in PowerPoint, but don't actually deliver much business value
because nobody is actually measuring business impact outside of how impressive
it sounds. When the Data Science team tries to focus on projects that require
more work that deliver more value, they're shot down because they don't fit
into the "monthly management update" mentality.

You need a good management structure that realizes that quick data mining
exercises are a stepping stone to delivering longer term value. They are not
the ultimate goal.

------
antt
As someone who has been employed in this space recently: No there is not.

There is however a lot of middle managers who have heard the term and want to
pay crud vba developer wages for people to make pretty graphs in excel.

The credential inflation is also getting ridiculous. I have an MSc and I don't
see how me being able to do quantum chromodynamics at one point in my mid 20s
adds value to a business ten years later.

~~~
collyw
That's what I am thinking. Any half decent software engineer could probably
produce the results they need from a few SQL queries. But "data science" is
hot at the moment.

~~~
i-am-charmander
Comments like this reinforce the idea that data science is too broad a term.
__Everything __data-related is thrown into the "data science" bucket.

You describe what I'd call business intelligence or even advanced analytics.
SQL jockeys (not a pejorative) really slicing into the data and presenting it
in meaningful ways. There's usually exists some science behind these
exercises.

Then there's machine learning, which also gets clubbed into the data science
camp. Try capturing a convolutional neural network in SQL - you might go bald
from pulling your hair out. It's totally __not __the tool for the job.

While perhaps any half-decent software engineer could maneuver his/her way
around the great ML libraries out there to produce a CNN (or something of the
like), I would hesitate to give them a project that involves _true_ science
(bring out your pitchforks). Experimental design, hypothesis testing, etc.
While being a SQL expert could help turnaround time and enable you to test
more interesting hypotheses, I wouldn't leave most DBAs to develop and carry
out an experiment.

So I guess my point is "data science" is too broad and overused a term to be
meaningful.

~~~
ummonk
A lot of "machine learning" is the more traditional statistics-based data
science, not deep learning using a CNN or something...

~~~
i-am-charmander
I understand that, but my point is good luck doing a lot of that "statistics-
based data science" using just SQL.

------
DoofusOfDeath
It would be nice if the story touched on compensation levels.

If all of those companies offered $400k/year, with guaranteed employment for 7
years, they would suddenly find no shortage of data scientists.

Although other industries might suddenly wonder what happened to all their
statisticians / physicists / etc.

And two years later there would be a glut of data scientists who had just
completed their master's degree.

~~~
wenc
Not every labor shortage problem is solved by adjusting compensation levels
upwards.

The compensation has to match the value created.

~~~
jlelonm
Why is that, economically speaking? If there's a labor shortage, doesn't that
mean it's undervalued in the first place? Forgive me if that sounds naive, I'm
no economist.

~~~
wenc
Because it makes the assumption that the system has no constraints, which is
often untrue.

First of all, you have to ask yourself _why_ there is a labor shortage. If the
underlying reason is insufficient pay, then sure, increasing pay will fix it.
This is often the case for fungible talent -- incentivize something enough,
and people will shift resources to it. However in many situations, certain
types of work are not fungible.

There are natural barriers to entry and qualification issues. Surgeons, for
instance. You can incentivize and compensate all you want, but the fact is,
not everyone is cut out to be a surgeon, so you have a funneling effect. It's
not even a matter of pay.

Then there's desirability issues. Deep sea welding is a highly specialized
(and dangerous) trade that pays handsomely, but not everyone wants to do it.

Some physically demanding jobs also have natural attrition issues in spite of
compensation. When the inflow of talent is smaller than outflow over a long
period of time, a shortage results.

There's also a training and timing issues. Let's say it's 2011 you want
someone who can build the kind of infrastructure that powers Netflix, in 6-12
months. The talent and experience pipeline would still have been brewing at
the time, so there's going to be a temporary shortage of talent and experience
until folks gain experience and mature in the field. In that instance, you can
still hire and grow personnel over time, but there would still be a shortage
of existing talent.

Talent pipelines take time to build, are highly dependent on talent pools
available and the ecosystems around it. Just throwing money at the problem
doesn't always work. Compensation is just one end of it (the opportunity end);
there are also significant long-term investments needed on the other end (the
cradle end, which includes education, development, etc.). Texas Instruments
did this -- they funded what eventually became UT Dallas... but it took many
many years and the outcome was uncertain.

There's geographic issues. To use an extremely unlikely example, let's say you
wanted someone at the level of Jeff Dean or Sanjay Ghemawat, but you would
need them to relocate to Podunk, Iowa. Very few people at that level would
want to relocate for any amount of pay, hence a shortage. I realize this is a
pretty extreme example, but I worked at a company in an undesirable part of
the country and it was difficult to get truly talented folks to move out there
even with significant premiums on compensation. Now you can keep increasing
compensation until you get someone who's willing to move, but they're usually
not the kind of talent you were looking for in the first place. Also there's a
break-even point at which the compensation doesn't make sense for the value
the position is likely to generate, so companies will just not hire.

My point is there are all kinds of _real_ and _complex_ reasons why increasing
compensation alone will not solve all shortage problems.

~~~
DoofusOfDeath
Thanks for the great rundown of some factors! I would like to take issue with
just one part:

> Very few people at that level would want to relocate for any amount of pay,
> hence a shortage.

I'm not sure if you're using hyperbole or not. But I'm guessing that if you
paid $1 million USD / year, there would be no practical shortage of data
scientists willing to live in Podunk, Iowa.

~~~
wenc
There is a cost-benefit proposition to every hire.

Unfortunately, the issue there is that the majority of data scientists don't
generate $1 mil of value (exceptions exist of course), and so it's difficult
to justify that level of compensation to management, which means the position
may never get created in the first place.

Absent an existential threat to their bottom lines, companies will just muddle
on without hiring.

This may be different in high growth companies, but in most traditional
companies that aren't sloshing around in VC cash, to increase headcount you
need to provide a value justification vis-a-vis salary (unless it's for cost
center positions, but even then..)

There's also an underlying assumption that talent flocks to the highest
bidder.... but most HR folks will tell you that it's more complex than that.
There are many quality of life issues that come into play, like weather, peer-
group, spousal happiness, etc. Money doesn't buy everything, and humans aren't
optimizers but satificers.

I'm a sample of one, but for me, I would absolutely not move to Podunk, IA for
a $1mil salary. I'm happy taking a lower salary living in a city and
intellectual milieu that feeds me. The kind of person who would take the $1mil
may likely not be the kind of talent you want.

------
madenine
The market for serious data scientists is growing, but is not as dire as these
articles make it seem.

The private sector has wrapped 'senior data analyst','BI developer','data base
administrator', and 'data engineer' into 'data scientist', along with the
ML/AI/stats roles you would expect.

So sure, when you put all the roles that touch data into one basket, and we're
in a world that demands increasing familiarity with data systems and data
analysis techniques... a gap is going to appear. Color me shocked

Typical data scientist role I see posted is looking for: \- SQL \- AWS/Azure
\- Tableau/Power BI/Other BI tools \- Python/R \- Increasingly Hadoop/Spark

With Java/Scala/C#/C++ listed as 'nice to haves' along with 'machine learning'
and 'big data'. These are not research roles, and while they often want a
masters degree or more they really don't need them.

If you fit that bill you can probably get a role paying $95-120k just about
anywhere in the US. You'll wrangle data, make pipelines, build dashboards and
occasionally deploy a model or two when the planets align.

If you want to/are capable of doing more ground breaking/cutting edge work,
these roles will crush your soul in 8-12 months.

~~~
antt
>If you fit that bill you can probably get a role paying $95-120k just about
anywhere in the US. You'll wrangle data, make pipelines, build dashboards and
occasionally deploy a model or two when the planets align.

If you expect to pay that little to anyone who knows the above to any degree
you will have a bad time.

A number of projects I have joined absolutely look like someone treated them
as a way to learn the technology before moving onto better paying jobs,
leaving the business with a terrible mess that's largely unfixable without a
rewrite. You're better off hiring one developer that knows what they are doing
at above market rates than three who don't for the same price.

~~~
BeetleB
>If you expect to pay that little to anyone who knows the above to any degree
you will have a bad time.

I believe you misunderstood his point. The $95-120K figure is for people who
do _not_ do ground breaking research. Anecdotally, I know people who get hired
for data science who are doing very little data sciencey stuff (a bit of data
manipulation/slicing and lots of plots - almost no actual ML). These people
should not get paid more than a typical SW developer, and decent SW developers
should get paid more.

~~~
antt
> \- SQL - AWS/Azure - Tableau/Power BI/Other BI tools - Python/R -
> Increasingly Hadoop/Spark

Is above and beyond a typical SW developer. That's three languages and two
dsls.

If you expect that on a 120k salary, you will be what you pay for, e.g. a code
monkey living on peanuts.

~~~
BeetleB
First, I don't think he meant they expect you to know _all_ of these - just a
subset.

Second, most people I know who know R or Pandas or other Python numerical
stuff (NumPy/SciPy) are _horrible_ programmers. They're good at numerical work
- but that doesn't translate to good coding skills. The bar is not that high
to know it.

It's somewhat similar with AWS/Hadoop. Most people I know have taken learn
these (online training, in person classes, etc) with very basic SW skills.

I don't doubt expert programmers exist who know these technologies - I'm just
pointing out that it's not that hard to learn some of these. Plenty of people
learn these without knowing much SW. To be a good SW developer takes a lot
more.

>If you expect that on a 120k salary, you will be what you pay for, e.g. a
code monkey living on peanuts.

You must be in the Bay Area. A 120K salary is above the median for SW
developers for most of the country, and grants them a great lifestyle: Big
house, two cars, vacation travel, etc. Hardly "living on peanuts".

The GP was clear he was not talking about the Bay area:

>If you fit that bill you can probably get a role paying $95-120k just about
anywhere in the US.

Finally, the point is: If you studied actual Machine Learning, he is pointing
out that these jobs are mostly not that, and you will just do a boring job.

~~~
antt
>You must be in the Bay Area. A 120K salary is above the median for SW
developers for most of the country, and grants them a great lifestyle: Big
house, two cars, vacation travel, etc. Hardly "living on peanuts".

I'm not in the US currently. I have worked with teams in New York in finance
and the wages expected were multiples of 120k. Likewise in London and Honk
Kong.

~~~
BeetleB
Most of the US is not California or the NE.

When someone says "most", they mean the outliers are not included. When one
says "most Americans can't point out Egypt on the map", it's rather pointless
to give examples of PhD's who can.

~~~
mmt
That made me wonder, though, in this particular context, are these really
outliers?

Even just counting population, the Bay Area's CSA is 8.8M, NYC is 23.9M,
Boston is 8.2M and LA is 18.9M. That's already over 18% of the population,
just in the major urban centers of California and the Northeast.

It's not much of a stretch for the percentage of tech jobs to be in those
areas, and less of a stretch to imagine that the vast majority would be in
some kind of high-cost area.

I wasn't able to come up with suitable web search terms to find any numbers,
but perhaps someone else has.

~~~
antt
Throw in Seattle and there are no tech jobs outside those areas to a first
approximation.

I don't know where the people above are getting hired, but the only places
I've ever seen outside those major cities are incidental programming jobs to
babysit VBA code that runs some business that has nothing to do with
computers.

~~~
BeetleB
Denver, Austin, Houston, North Carolina, Chicago, Atlanta, Salt Lake City,
Boise to name a few. Granted, some of these have recently become pricey, but
still nowhere near Seattle levels.

Look at all big companies (and not just the recent Internet ones of the last
20 years), and look at where they have sites. Jobs in most of these places.

~~~
mmt
> Look at all big companies (and not just the recent Internet ones of the last
> 20 years), and look at where they have sites. Jobs in most of these places.

But how many of those jobs are technical/programming (in those places)?

Even by population alone (Chicago, Houston, and Atlanta making up the bulk),
that's under 10.7%, less than CA+NE (and only half of CA+NE+Seattle?). That
persuades me that these lower-cost areas are the exception, not the rule.

~~~
kthejoker2
Not sure why you'd compare Houston to all of California?

[https://en.m.wikipedia.org/wiki/List_of_metropolitan_statist...](https://en.m.wikipedia.org/wiki/List_of_metropolitan_statistical_areas?wprov=sfla1)

Just looking at top 50, you've got a real geographic spread - about half of
the people are in CA/NE, the rest are split pretty evenly between the four
quadrants...

~~~
mmt
> Not sure why you'd compare Houston to all of California?

I believe everyone in this sub-thread has used "CA" to mean the SF Bay Area
plus greater Los Angeles (and certainly I have). Similary, "NE" was just
shorthand for the big cities therein, not the whole region.

Ultimately, population is just a _very_ loose proxy for number of tech jobs.

------
tw1010
I feel like I hear just as many people getting degrees in "AI" nowdays as
plain CS degrees. A much higher percentage than five years ago. It feels like
everyone and their grandma is minoring or majoring in AI or machine learning.
And when I talk with companies about it, they're lukewarm at best.

I really wouldn't bet my personal career at this point on going into this
area. It'll be filled to the brim sooner than you expect. It's a much better,
and just as intellectually satisfying, bet to just go into plain traditional
CS in my opinion.

(A counter argument is that I felt about the same sentiment around three years
ago, but my intuitions doesn't appear to have manifested, so maybe I have poor
intuitions, or I'm missing some piece of the puzzle.)

------
NatW
I've heard it said, "A data scientist is a better statistician than a typical
programmer and a better programmer than a typical statistician."

I 'graduated' from the Data Analyst Nanodegree @ Udacity in my spare time, and
have experience with both. I lack a background in math beyond university
Calculus, though, so that seems the major barrier for me to get a foothold in
the industry.

~~~
friday99
The other way to phrase that is "A data scientist is a worse programmer than a
typical programmer and a worse statistician than a typical statistician." If
you need a programmer, hire a programmer. If you need a statistician, hire a
statistician. I wouldn't ever hire someone who took a bootcamp that is "a
better plumber than a typical doctor and a better doctor than a typical
plumber."

------
dostres
I’d have a hard time finding a job in ML for $300K but I’d have an easy time
finding a job in PHP contracting for $350K. This is despite having 10 years
experience as a ML practitioner.

I quit my last job after bringing in over $100M in profit and only getting a
$30K bonus.

Now it would take ~$500K and an ‘eat what you kill model’ for me to consider
going back to work.

These days I make my money from licensing ML products which gives me a lot of
time to study - which is what I enjoy doing.

~~~
gcb0
where do you find php contracting for that which lasts long enough to make 350
a year?

~~~
dostres
Initially one of those elite consulting marketplace things. And then my own
pool of referrals. I hate PHP so I set my rate higher on those projects and
people still pay it.

~~~
j88439h84
What is an elite consulting marketplace? Where do I find one?

------
techstrategist
Can anyone with industry hiring knowledge comment on this gap?

Maybe I’ve misunderstood but from HN / other online discussions it seems that
employers in Data Science have been increasing education requirements to MSc,
PhD, and so forth for many jobs that more appropriately require a strong
statistical understanding combined with rigorous data processing skills and
some imagination. Is this a real “skill gap” or an attempt to create
artificial scarcity of hot jobs?

~~~
isoprophlex
IMO, as companies' data science capabilities mature, people with actual skills
are needed.

5 years ago DS was hot and new. As a middle manager you would call up a
consulting meat farm and ask for data science consultants: presto, you have a
data science team. These people would, likely, be random BSc's with a SAS base
certificate.

In 2018 data science, and data driven decision making, is still hot, but
hiring managers know they need people with a formal education. So, education
reqs go up and scarcity increases.

~~~
conjectures
+1 because mooc style learning alone just isn't enough in most cases.

~~~
albemuth
What would you say is missing from a mooc-based training?

~~~
conjectures
Having looked through the contents of a couple of data science moocs they
often try to give intuition without math.

When trying to put it into practice, if we go the slightest distance off piste
we are lost without the math. So it's a fragile form of knowledge.

If we take someone who's already strong at math from some other training and
teach them more stats/ML concepts and intuition, then great. But the value add
of the mooc learning is quite low in that scenario.

The best alternatives (barring a formal course) imo, are text books that have
exercises like Bayesian Data Analysis by Gelman.

------
acbart
I've had a few friends express interest in pivoting their domain knowledge +
small programming knowledge into a Data Science job. Are there many success
stories or guides out there for those who make such a transition? I went the
formal route of getting the CS degree, so I never really thought about how you
turn a non-CS degree into a CSy type job.

~~~
ThePhysicist
Most data scientists I know don't have CS degrees but instead a background in
physics, biology, math, economics or statistics.

For a data science role focused around exploratory analysis, model building
and idea generation (as opposed to a more engineering-focused data scientist)
I'd say that a basic understanding of programming concepts and familiarity
with the most essential tools (version control, Linux, Python/R) is probably
enough to be accepted into a data science role. Data science degree programs
are still quite novel so most companies don't expect to recruit people with
such a degree, hence there are good opportunities for people with a STEM
education I'd say.

~~~
bobmarley1
I've actually seen many many job postings looking for people with a PhD in
Data Science which is truly mindblowing because there are only 2-3 such
programs in the Country and most of them just started so none of their
students are even on the job market yet.

~~~
PAClearner
ummm you are probably wrong. those job postings probably want people with PhDs
in Computer Science/Statistics/Applied Math that did hard things with data.

------
JustSomeNobody
Any time I see businesses complaining about shortage of skilled labor, I read
it to mean they aren't willing to pay enough to attract the skilled labor they
want.

The formula is simple. Businesses are in business to return as much money to
investors as possible. IE: they're all about the money. Employees, more and
more, are saying, "So am I. You want yours, so I want mine." Pay more and
you'll attract more talent. Simple.

------
itg
In my experience, the title 'Data Scientist' has become to mean data analyst
at most companies, meaning working with Excel, Tableau, and SQL. Maybe R if
you're lucky.

Companies doing ML/AI will usually have a small team of Research Scientists
who mostly hold PhDs, and a team of supporting ML Engineers.

~~~
tixocloud
I’d be more inclined to think that at a startup, because of the lack of
infrastructure, a data scientist will do a lot more than research.

~~~
mlthoughts2018
It’s even worse at established companies because data scientists still only do
the data plumbing and simplistic analytics tasks, but not due to anything
reasonable, like the “many hats” needs of a startup, but instead because of IT
dysfunctiom and bureaucracy.

~~~
tixocloud
Think it depends but it does happen. We’ve been able to carve ourselves out of
IT with our own infrastructure and our data scientists specifically focus on
research and analysis. The only issue is when we try to get anything to prod
and we hit IT.

~~~
mlthoughts2018
> “The only issue is when we try to get anything to prod and we hit IT.”

Which inevitably means the one person on the data science team who is good
with linux and docker suddenly becomes the IT wizard, and their time gets
sucked up by having to find ways to go around utterly stupid barriers and
tactics used by IT to avoid doing work to help you.

Source: I am currently this person for my team.

~~~
logosmonkey
As someone who is about to be tasked with building this sort of infrastructure
for a hospital system what would be your dream architecture? I hate IT hurdles
and I am hoping I can avoid building them into our infrastructure.

~~~
mlthoughts2018
The number one thing is to make containers a first class deployment and
provisioning artifact. As long as dev teams can control their own containers,
they will be able to do what they need no matter how arbitrary or assumption-
breaking.

Do not ever require dev teams to go through IT to get their chosen tools
deployed to the right places or with the right resources provisioned. Never.

This is the root of all evil with infra teams: if they see themselves or their
mandate as being gatekeepers of provisioned resources, then dev teams have
lost and you as a data scientist / ML engineer, you’ll never get your work
done.

As an ML engineer, I want to define the entire runtime and development
environments of any analytics artifacts or web services that I create, and to
change these environments as needed, as indicated by what’s required to get
the job done.

Let me define containers, hook them up in whatever CI tools are used, push and
pull them from some internal container repository, and describe the
configuration for the resources they need. Offer that as the contract to dev
teams and then infra’s job is to maintain the underlying data center that
physically supports running the containers and occasional hand holding for
special exceptions, networking, secrets management, and cost tracking.

~~~
tixocloud
Very well said. I can’t stress the container environment enough not to mention
the ability to control the toolset.

------
citation_please
As someone who has been producing value in a data science/machine learning
role for multiple years, it's disheartening to see comments that I may be
blacklisted from positions due to "only" having a bachelor's degree.

Somewhat non-humbly, I was valedictorian at my high school, I triple-majored
at a respectable Big 10 school, I actively use all 3 majors on a daily basis,
in a foreign country, and sometimes in a language that is not my mother tongue
(as an American).

I can't justify spending time and money on a master's degree (millennial
wealth problems) where many courses would just be putting a formal, academic
spin on ideas that I'm familiar with from a practical business-value-producing
point of view.

Any advice on how I can effectively jump off the black-lists?

~~~
laurentl
Maybe a bit (a lot?) out there: why not go for a business degree instead, e.g.
EMBA?

You mention “foreign country” so I’m guessing you can have access to good
curriculums without paying the US premium on education (or just go for an
online course).

Pros: the time and money is not “wasted” as you actually pick up new skills;
the MBA card should be enough to trump any education requirement; and you
become that most desirable of hybrids: the tech/data guy who can talk business
(or vice versa).

Cons: significant time (and money) investment; doesn’t help you get expert DS
jobs (you’d be aiming for team/program manager, consultant, etc)

My own experience: completed an EMBA in 2017. Ranked in FT’s top 10, the
program cost was around 50 k€ (it’s increased a bit since) and I was able to
get 25 k€ of outside funding. The program I followed lasts 2.5 years, meaning
I was able to do it while keeping my job (and having a kid) without losing my
sanity or my wife. Landed my dream job just before completing the curriculum
for a nice 40% pay increase (not saying the EMBA alone had that effect, far
from it —but it definitely helped).

~~~
citation_please
Huh. This is actually a route I hadn't considered. Thanks for pointing it out!
I will definitely consider it as I continue researching my next
opportunities...

~~~
laurentl
Glad that helped ;)

If you want more feedback on my personal experience, feel free to reach out
(will update my profile with an email address).

~~~
Synroc
Is there any difference in terms of curriculum between the EMBA and the MBA?
As in would people consider the EMBA a "lesser" MBA so to speak?

I'm an engineer looking at MBAs right now too, but it seems like a huge
investment.

~~~
laurentl
EMBAs are usually part-time over 18 to 24 months, MBAs are full time over 12
to 24 months.

So an MBA naturally has a bigger (as in more in-depth) curriculum than an
EMBA. It is also a significantly higher investment in terms of time and
opportunity cost, since you're not getting paid during the program.

EMBAs compensate with 1) more experienced participants (so in theory you don't
need the introductory classes) and 2) a lot of pre- or post-readings (e.g. my
corporate law module was 12 hours in the classroom, but you were expected to
have read the 400-page book, and the numerous case studies).

But the bottom line is that you don't go into as much detail as you would
during a full-time program. OTOH, since EMBAs are attended by "senior"
employees (managers / VPs / directors / etc), and because they're part-time,
what you learn is usually directly relevant and applicable in everyday work -
and you usually get to work on real-life problems (yours or your teammates)
during classes.

I'm not the best placed to say whether an EMBA is considered a "lesser" MBA.
They don't really fill the same niche. An EMBA is a career booster if you're
say a technical manager and want to move into business or senior management.
An MBA is when you haven't started working (or are still junior) and are
looking for a fast track to C-level, or to work in a specific area (e.g.
consulting, finance, etc.). So basically MBA vs EMBA is mainly a function of
your current experience level.

------
jakecrouch
I view modern AI as a combination of things that are too early and too late -
data science hasn't changed much since the 1990s, but people have a
misconception that there is still a lot more to do in data science because of
the undeserved excitement around deep learning.

~~~
sgt101
Errm - I am pretty sure that we didn't have MCMC or causality analysis in the
90's.

Or R-studio.

~~~
johnmyleswhite
Perhaps I've misunderstood your desire to be sarcastic, but I think your
timeline is off. To give two concrete examples, Geman and Geman's "Stochastic
Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images" paper
is from 1984 and Holland's "Statistics and Causal Inference" paper is from
1986.

------
TadaScientist
You don't need to have a CS degree to be in "Data Scientist". In fact, I would
argue that you need a statistics degree. There is a difference between a data
scientist and an engineer.

~~~
disgruntledphd2
The problem with stats degrees is twofold:

1) They often lack experience with messy (i.e. real) data 2) They sometimes
lack experience with programming.

Data science, as currently practiced requires some CS, some stats (with a
focus on experimental design/observational analysis) and communication skills.

These skills are rarely taught together in undergrad, but lots of PhD's
acquire them by accident, hence the propensity of PhD's to end up in these
roles.

------
En_gr_Student
Who are you kidding? They aren't hungry. There is lots of propaganda flying
around, but they aren't really even looking hard.

HR games the system.

------
another-cuppa
I was hired as a data scientist for a large company that thought they needed
data scientists. They didn't and I added no value to the company. I left after
about a year. If there are many people like me then that might explain the
shortage.

------
Max_Mustermann
How do you see the chances of someone without a degree getting an entry-level,
low-paid, remote job in this space? What would be things that could help out?
Certifications? Nano-degrees like Udacitys? A portfolio of public work?

~~~
clu1590
It's highly unlikely. There is a lot of competition for entry-level jobs and
not many remote positions available. The barriers to entry are exactly
academic qualifications over certifications and portfolios.

------
duchenne
I think that the challenge to hire a good data scientist is that it requires
skills in both:

\- hard science: strong statistics or ML to analyze data, some CS to organize
the data.

\- soft skills: strong business sense to understand what is important, some
communication skills to explain it.

A data scientist needs very good skills and experience in both, but they are
somehow anti-correlated in my experience.

~~~
tixocloud
That’s why we usually have a team that complements each others’ skill set. The
data scientist while great to have everything does not necessarily need to
have everything. I tend to focus on their core strengths and depending on what
their growth/career aspirations are, help to develop the skills. If it’s soft
skills, happy to help them develop that edge.

------
tmalsburg2
I'm teaching stats and scientific computing at university. I thought that data
science might be a good exit plan in case I get feed up with academia.
However, even though data scientists seem to be highly sought after in Germany
(judging from what I see on linkedin), the salaries are fairly disappointing.

------
adiusmus
Ah yes. A shortage. Someone didn’t do their planning. Bubble bubble tool and
trouble. Or is this about more visas? And paying people less.

Seriously though, what exactly is a data scientist? What is the requisite
skill set? Is this masters level entry or would my feline require a Pointy
Headed Degree upgrade to play this game?

~~~
adiusmus
Masters and PhD are required for particular employers. Others not so much but
there are caveats. High probability of being the “data” guy who is then made
to be the database admin.

Better summary at: [https://blog.insightdatascience.com/from-phd-to-data-
scienti...](https://blog.insightdatascience.com/from-phd-to-data-
scientist-17209c66a9d9)

------
Aeolun
Maybe they’re all desperate for good data scientists? The truly good data
scientists that I’ve found have actually been scientists.

Literally all “data scientists” I’ve found in a corporate environment have
been so useless it is depressing.

~~~
ccdsacc
My experience is that most valuable data scientists are those who are good
engineers.

------
singingfish
I'm available for the right data science gig. Somewhat unconventional CV
available on request. I'll try to bring my competent but intellectually foggy
business analyst colleague along for the ride if you like.

------
olivermarks
I smell buzzword HR bingo

~~~
thanatropism
Yeah.

I find "data science" a cringey expression and I'm not looking for a job
currently, so my LinkedIn emphasizes my strengths according to my ego/self-
appraisal, not a "desperate" reaching out for fashionable job titles.

------
pvaldes
Maybe oral communication skills are overrated. Who knows? People currently
talk a lot also with their hands.

