
Tips for Hiring a Data Scientist into a Tech Company - straun
https://blog.infer.systems/2020/01/10-tips-for-hiring-data-scientist-into.html
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tempsy
I feel like pursuing Data Science as a career is such a career trap.

Most companies vary wildly in expectation for these roles. You might be asked
to spend most of time data engineering (like making ETL pipelines in one), or
build a bunch of basic ML models , or spend all your time doing SQL queries
and building dashboards in another.

It’s silly and would never recommend anyone pursue that path as a lifelong
career. Learn to code if you don’t know already, and just go into a software
role that is data or ML focused.

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mygo
You could say the same about recommending someone getting a computer science
degree. You may be setting yourself up for disappointment if you’re only in it
for the job you can get afterwards, since the work that you’d be doing in the
job would at least somewhat depend on the work that needs to be done.

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Aperocky
Having worked in data science, my advice would be to hire people who has
coding ability.

Who can write working and reviewable code.

Not because statistical knowledge is not important, but because coding skills
are often overlooked. I see no role in data science that shouldn’t need at
least a junior software engineer level of coding skill.

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throwaway713
As a data scientist, I agree with this. On my team, the data scientists with
the largest impact seem to be the ones who are sort of hybrid software
engineers / data scientists. I’ve noticed these people tend to swap between
the two titles occasionally (re-interviewing within the company for one role
or the other).

I think if I had my own company I’d create a position like “scientific
software engineer”, which would basically be a SWE with a scientific or
statistical background. It would de-emphasize pure analysis in favor of
directly applied research in the form of production code. There are some
similar titles out there (research scientist, ML engineer), but they don’t
quite capture exactly what I have in mind. “Applied scientist” would probably
be the closest.

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ska
I've done this at a couple of places, creating "scientific developer"
positions. It works reasonably well but has some gotchas. The biggest one is
identifying people who are truly interested in developing some depth in both
areas.

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mobileexpert
This thinly veiled advertisement gives me almost no confidence that the
posting firm has ever met a data scientist much less possesses expertise in
how to effectively recruit and build a function for them at a small tech
company.

The first things to understand is if you are hiring for a DS role to support
product development/understanding, growth or marketing, advanced BI, core
product features (eg ML in your offering) etc. What VP your DS would report to
in a 500 person version of your org chart makes a huuuge difference in
understanding what skills and aptitudes you are looking for.

Design the position and responsibilities now and in 2 yrs for the ideal
candidate, then design your hiring strategy.

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awinter-py
> you might find that you are not looking for a Data Scientist at all, perhaps
> you are in fact looking for a Data Engineer or a Data Ops person

Strong agree

Managers who haven't been through this process once will hire a smart stats
person who can't operate without a full-time engineer pulling production data.

Which can be crippling for small teams. The alternative is to make your PM do
their own data work or promote a product-minded engineer into this role.

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bradleyjg
Your PM can make you good looking charts and do basic summary statistics
(mean, median, percentiles, etc.) I would call that business analyst work.
It's probably good enough in a lot of situations. But if you actually need
statistical analysis, there's no substitute for a statistician. Even working
scientists in the hard sciences routinely screw up their stats and _should be_
(but mostly aren't) consulting with a staff statistician for their papers.

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scottlocklin
This is a meaningless checklist; the only actual useful advice is to get the
word out at meetups and such. The way you hire a data scientist is much the
way you hire into any other team.

If you have a data scientist who is worth anything, he should be given
responsibility for building the DS team. If you don't: you probably shouldn't
hire one. If you must: hire one with experience, if necessary, as a consultant
to build your DS team.

Putting physicists at the top rank is also a bad piece of advice; people with
experience are at the top rank for DS. If it's pure fresh meat, you're better
off with applied math people than physics people (and I am physics people).

