
If you want to hire an experienced data scientist, be prepared to pay - mccricardo
https://www.linkedin.com/pulse/you-want-hire-experienced-data-scientist-prepared-pay-richard-downes?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3Byx4LCWwV5ZSxDRUe6NZTEQ%3D%3D
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brogrammernot
I feel like this could be universal.

If you want to hire an experienced, production and value adding _______ be
prepared to pay.

It's like the phrase "You don't pay a plumber to bang on your pipes, you pay
them to know where to bang." (yes, that's from Suits)

Anytime you choose cost over experience you end up paying more than you would
in the first place by the end.

Obviously there's exceptions to that, but if you're going to be cheap on a
component or person that you need you will definitely regret it later.

~~~
ryanwaggoner
The plumber quote is likely ultimately based on this incident between Charles
Steinmetz and Henry Ford:

[http://www.smithsonianmag.com/history/charles-proteus-
steinm...](http://www.smithsonianmag.com/history/charles-proteus-steinmetz-
the-wizard-of-schenectady-51912022/)

One of my go-to stories when telling freelancers not to charge hourly :)

~~~
gmarx
Thanks for the link. I thoroughly enjoyed that. I never thought about the
character behind the name. heartwarming

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probinso
I have met many people who call themselves a data scientists. it is very easy
to call yourself a data scientist, and justify the label.

if the vocabulary of your community doesn't support your needs, then you need
to modify your vocabulary. Swear words like 'data scientist', invite the idea
of casting a wide net with a large set of poorly defined skills to arbitrarily
select from.

I appreciate the communities' use of the term 'data engineer' to quarantine
out some of these skills. For those writing a job description, or vetting
candidates, these words really matter. when you muddy the soup, by expanding
definitions, time gets wasted.

~~~
alexchantavy
Is there a commonly accepted standard for qualifications of a data scientist,
are they expected to be software engineers with PhDs in something
statistical/data-oriented? I've heard mixed things and am interested in
getting clarity on this - sometimes they have CS backgrounds, sometimes they
have non-CS backgrounds, some are competent programmers, others can't write a
line of code, etc.

~~~
laughfactory
Nope is the short answer. But in general you're expected to have a grasp of
the fundamentals of statistics, some software engineering, Python and/or R,
knowledge of the various algorithmic approaches, and a host of traits which
really make a data scientist: curiosity, persistence, determination,
flexibility, adaptability, detail oriented, conscientious, really like a
challenge, resourceful, good interpersonal skills, ability to convey the
complex in simple terms... In many ways I think it's almost more about the
traits than the training.

But yeah, where I've worked that's generally what we look for in candidates.

What is astonishing to me is how there seems to be 1) a dearth of candidates,
period, and 2) candidates we can dig up miss scheduled calls, show up late for
interviews, interview very poorly, turn in poor quality take home exercises
(an exercise which essentially just covers the basics), have really crappy
resumes (typos, horrible layout, inconsistencies with LinkedIn profile,
etc...)--and these are folks with experience as statisticians or data
scientists. Amazing.

We don't ask anything deep or complex either, yet we've had a really hard time
finding people.

~~~
yesiamyourdad
The chief data scientist at my last job said a Data Scientist knows more
programming than your average statistician and more statistics than your
average programmer. There's this venn diagram he used to show with the
intersection of skills for the different disciplines involved - some math,
some engineering, some communications.

I think there's also an intersection with devops skills, maybe less important,
but your hardcore statisticians usually put zero thought into operational
considerations. Really the last bastion of "works on my machine" thinkers in
the computing world. I just finished the Coursera "Reproducible Research"
course and I was really struck how many of those principles parallel good
software engineering practices - use source control, document through code,
separate your environment from your code, automate as much as you possibly
can, etc. I've been a software engineer for 20+ years but I want to get into
data science partly because I've always been a data head, just without the
theoretical background to do really interesting work, but also because I think
I can bring some of the software engineering skills to bear.

Also, with grading peer's work on Coursera, I really realize that a lot of
these candidates need help with their English and presentation skills. Many of
the students put no work at all into the presentation, I imagine that's going
to serve them poorly in the working world.

~~~
johan_larson
The way I've heard it from others in this forum, is that a DS is a combination
of three jobs. They are analysts, in that they can work with data and squeeze
insights out of it and they know enough about the business to know that the
numbers mean and what differences matter. They are software developers, in
that they can build actual software solutions to access and manipulate data,
rather than relying purely on shake-n-bake existing tools. This helps them
deal with very large data sets that are beyond conventional analyst tools such
as spreadsheets. And finally they are experts in stats/ai/math who can build
and evaluate sophisticated mathematical models.

It seems to me that's an awful lot to fit between one pair of ears.

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mdekkers
I recently read this quote somewhere: "If you think a pro is expensive, wait
until you have to fix the work of an amateur"

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cheez
What are the salaries for "data scientists"?

~~~
probinso
7.50$ /hr

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pvaldes
If you want to be tagged as an experienced data scientist be prepared to work
for free for several years.

Or be payed in "credits", that is the same thing.

~~~
pvaldes
s/payed/paid/

