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So you’ve decided to leave academia, or are perhaps just thinking about doing so. Welcome to the dark side. I made the transition a few years ago, and since then I’ve gotten a number of questions about how to do it. Hence, this article. First of all, I’m going to assume you are looking for a technical job, and I’m also going to assume you are coming from a technical field. When I left my postdoc I focused on finding jobs in technology and in finance.
A number of people seem happy about leaving academia and doing consulting (e.g. McKinsey), but I don’t know anything so I won’t comment. Also, if your degree is in English, the best I can do is point you here.
Building your Skillset
Good news - you’ve already got a solid quantitative background. This opens up a lot of doors for you. Now you just need to focus on building up some practical skills. They are a lot easier than algebraic geometry or solid state physics, but they are necessary nonetheless.
Most jobs open to quantitative people these days involve programming, so it’s strongly beneficial for you to learn it.
To begin with, go read Software Carpentry. Right now. This document covers all the basic practicalities of dealing with code. Even if you plan to stay in academia, you should go read it, particularly if you plan to be a computational scientist. I’d differ from software carpentry in only one case: use git instead of subverson. A tutorial on git can be found here.
As far as programming languages to learn, I’d suggest Python to start with. This is because python has the excellent scientific Python andmatplotlib libraries, which matlab like functionality in a language suitable for use at work. You should also learn C++ - it’s fairly widely used in quantitative jobs, particularly in finance. It’s also very common for interviewers to ask C++...