

Ask HN: science grad student here, how can I prepare for a career in industry? - pivot_data_sci

Hi HN, I&#x27;m a regular here, posting on a throwaway for privacy.<p>I&#x27;m currently pursuing a doctorate in the life sciences. Originally I planned on pursuing the tenure-track path, but my experiences and the realities of the job market have caused me to reconsider. I do a lot of software development and statistics as part of my research, and I think a career combining data analysis and software development (a so-called &quot;data science&quot; job) would be very interesting and suited to my strengths.<p>I have an active GitHub account (and am a somewhat regular poster here) with projects in half a dozen languages and a decent math&#x2F;stats background. I&#x27;ve contributed code to a couple major Python libraries. But, I have no real work experience outside of academia to speak of.<p>I&#x27;ll be graduating in about 2-3 years. In the meantime, what should I be doing as a graduate student to improve my ability to land such a position upon graduating?
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dekhn
You have a long uphill battle, although in most cases, having a PhD and papers
you can point out can be useful. For example if I see your application, I'm
gonna ignore most of your resume and what you said you did in grad school,
find your first first author paper (and all subsequent ones), pull them up and
skim them, and decide if you have a clue.

cultivate friends who work in the software industry. listen to how they differ
from academia. Many people in academia are the world expert in some specific
problem, but have trouble transferring that knowledge outside the sphere.
Especially bad is people who are stuck in a long-term "this is how it's done"
attitude. I find a lot of people in life sciences to be terrible at big data
because they embedded poor learning/knowledge and can't unlearn it.

understand what industry does, why, and how. Industry exists to make money.
Period. You need to learn how to work in that kind of environment.

spend a lot of time learning to distinguish between "feeder" job positions and
"bespoke" job positions, and identifying the latter. Ex-scientists do best
with bespoke job positions that match their interests, rather than feeder jobs
that dump you in whatever "fungible" SWE position the company thinks you
belong in.

you should learn politics and how hiring works. There is no easy way to do
this, but your prof probably hires people and has some opinions. I had one
prof who had everybody in the group evaluate every resume in detail then rank
people by score. if the score was above a threshold, they were invited in for
interviews, and scored again (several times). This has been my experience in
industry as well. If you understand the metric, and can tailor your
application for it, you're in good shape.

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switch33
Do a real machine learning online class or some data analysis online class.
Some of the online classes from MOOCs can get you more of a sense of the
difference between doing software deployment/statistics for life sciences
versus doing it for other categories like market analysis, twitter, or
prediction.

Also there is Kaggle competitions if you do those well, or sometimes even
attempt them and rank decent I think you can qualify for some entry positions
by meeting employers interested in such abilities for data analysis.

Also promote yourself, instead of just saying you have a github account with x
projects, make landing pages with work that is well thought-out/done so people
looking for a project can find your page. Flask can make github pages hosted
from github repositories for instance. A blog works wonders for recruiting
potential from what I've heard.

