I recently received an email from a former research colleague who is thinking about moving from academia to tech. The following was my answer:
I recommend moving out of academia and find more fulfilling jobs elsewhere. Tech is a good sector, especially now.
What you need is a firm grasp of Python or R (better if you know both), SQL (we do not learn it in academia, but it is used everywhere in tech), machine learning (everything in the books Applied Predictive Modeling and Elements of Statistical Learning, to give you an idea). If you are interested in experimentation (A/B tests), you also need to know in depth about hypothesis testing.
Now, the most challenging aspect of the transition is finding the first job, since nobody knows you, you have no previous positions in tech, and what you have done in research – especially if you are not coming from either Computer Science or Economics – is mostly irrelevant to them, where them is companies, hiring managers, recruiters. Thus, everything you say or present during the interview process has to be connected in some form to business needs.
The second most challenging aspect of the transition is losing both the academic mindset and the academic jargon. In business (except for some teams in top tech companies that are doing academic research in industry), it is all applied science (more "applied" than "science"), and the jargon is coming from a mix of computer science (not predictors, but features; not estimating, but learning) and business language (not papers, but memos; and, if it is working, it is most of the time good enough for business purposes – which means that theoretical considerations take the back seat).
The easiest – although it still requires considerable effort – way to make the transition, is to take part in one of the many boot camps/incubators for data scientists. I strongly recommend Insight Data Science, since I have seen many alumni finding jobs at top tech companies right after completing the boot camp.