/data/universe/

Jessica Kirkpatrick wrote a great post describing her move from astronomical research to a data science job at Yammer.  (We were classmates in grad school.)  She discusses the technical skills she needed to learn (IDL alone won’t get you a tech job) as well as the differences between business and academic culture.  (Peter Fiske’s book Put Your Science to Work can help guide scientists through that cultural translation.)

In the comments, a finance recruiter added a key point:

The only thing I would add is come prepared to explain your motivations for wanting to move to industry. It’s important you can convince your future employer that you are moving for the right reasons.

I’ve heard that refrain a lot lately:  from panelists discussing aerospace careers for astronomers at the AAS meeting, from Jake Klamka of the Insight Data Science Fellowship, from professors describing jobs at teaching-focused colleges.  Apparently it’s surprisingly common for scientists to come into interviews with an attitude of “My research funding ran out, but I’m super smart and ready to make some real money.  What do you do here again?”

Obviously that approach insults your potential coworkers.  It’s far more productive to demonstrate your enthusiasm for the job and the goals of the company.  The more your background differs from the job you’re applying for, the more effort you need to make to show those hiring that you can do the job and that you’ll fit in the company culture [¹].

One of the staples of interview advice is to substantiate your claims with stories.  Accordingly, the best way to show your interest in your new industry is to point to previous work you’ve done in the field!  That could be an internship or volunteer work, a side project you did on your own time, a subject-focused blog you maintain, or attendance at a conference or meetup group.  (These explorations will also help you confirm your interest in the industry.)  Thus, smooth career changes out of academia begin with groundwork laid well before you actually change fields.

For data science, it’s particularly easy: as Hilary Mason points out, you can start doing data science right now!  There are many publicly-available datasets and apis, and many of the standard tools are open-source.  Brush up on your machine learning and join a Kaggle competition, or try making an engaging visualization.  Creative projects are fun, teach you new skills, and make you an easier hire all at once!