Low Dimensional Topology

September 29, 2015

Dispatches from the Dark Side, part 2

Filed under: Uncategorized — Jesse Johnson @ 7:10 pm

Back in January, I wrote a post about my experience with the differences and trade-offs between academic careers and private sector careers. In this post, I want to present some practical advice for anyone with an academic math background who might be seeking a non-academic job. This advice is based on both my own experience and the advice I found while trying to make the transition. Much of it is very similar to the advice you’ll find (in more detail) in a book called What Are You Going To Do With That? which I read early on and found very helpful. (The book is about finding a non-academic job with any type of PhD.)

The main problem that most newly minted math PhDs have is that they don’t know what non-academic jobs are out there, and what they might be well suited to. I certainly had that problem. So the first step is to find out. There are a few companies and a few types of jobs that specifically look to hire math PhDs, and you’ll see some of these advertising on mathjobs.org. But I found this to be too narrow a list and one that I didn’t find particularly appealing – the most obvious ones are the NSA and computerized trading companies.

Instead, I had much better luck investigating jobs that were looking for people from any background who had the types of skills that I thought had made me a good mathematician. This meant thinking in terms of general skills/abilities such as communication, understanding abstract/complex systems and managing complex (collaborative) projects. That opened the door to a much broader range of jobs, including both technical and non-technical jobs. Programming/software engineering (what I ended up with) is on this list, but it’s far from the only one.

Of course, that still leaves the problem of finding the jobs that meet this criteria. What I found most useful for this was something that the book I mentioned above calls an informational interview. The idea is simple: you ask someone who has an interesting sounding job to have a short conversation about their career. It helps if someone you know introduces you to them, and LinkedIn can be handy for finding such connections. You ask about what they do during the day, how they like it, how they got the job, etc. I know this sounds a but hoakey, but it’s also really interesting, and it turns out many people like talking about themselves.

The informational interview is explicitly no-strings-attached, i.e. it’s not a job interview and there’s no expectation that the person you talk to will help you get a job. But because it’s no-strings, people tend to be happy to do this. And because it’s a minimal investment for you, it’s a good way to explore options that you might not have thought you’d be interested in. I talked to a lot of different people in this phase, and probably wouldn’t have ended up where I am now otherwise; when I started my job search I was focused on jobs that were directly related to machine learning. But then I talked to an acquaintance from my undergraduate days who works at Google. He introduced me to a former computer science professor who had just started at Google, and who convinced me to apply.

The people that you have informational interviews with may also point you to specific job openings, and may even offer to refer you to someone who’s involved in the hiring decision. Perhaps not surprisingly, it turns out that applying for jobs on job boards is less effective than getting personal referrals, even if it’s from someone that you’ve only talked to over the phone. This may feel strange compared to the academic job search where all applications go through an official process such as mathjob.org. But keep in mind that mathematics is a small world. On the hiring committee at OSU, for the vast majority of applicants, someone on the committee personally knew at least one of the applicant’s letter writers, if not their PhD adviser. Outside of academia, it doesn’t work like that, so many companies use personal referrals to make up for it.

(Unfortunately, this reliance on personal referrals is a factor in the lack of diversity in the tech industry: since people tend to spend their time with others with similar backgrounds, individuals will tend to refer job candidates who are similar to them. My comments above are not intended to justify or defend this system. I’m just describing my understanding of how things work.)

But even without personal referrals, if you present yourself in the right way (particularly for jobs such as software engineer and data scientist that are in sufficient demand) old-fashioned job applications can still be pretty effective. With one job I applied for the old fashioned way, the recruiter e-mailed me back within an hour to schedule a phone call, though it turned out they needed someone to start before my semester was over.

Presenting yourself in the right way turns out to be the second tricky part. There are a few simple but essential things like understanding the difference between a resume and a CV, making sure your resume is at most two pages (or better yet one) and focusing it on skills related to the job in question, rather than on unrelated academic merits. It’s easy to get used to the fact that every job in a math department is pretty much the same – some combination of teaching, research and service – making it easy to point to previous experience as evidence that you’ll do well in the job you’re applying to. In the private sector, it’s more common to get a job in a position that you’ve never had before. Fitting an academic background into a non-academic resume takes some creativity, but again the key is to think in terms of broad skills. That means things like communication (teaching, research talks) and managing complex projects (your dissertation, long-distance collaborations). These will help demonstrate that you’ll be able to learn whatever it takes to succeed at the job, even though you’re going to be starting from scratch.

As an example, Google’s interview process consists mostly of working out programming problems where the only background knowledge requirement is (roughly) an undergraduate data structures and algorithms course. (Take a look at the official list of study resources.) What makes the interview hard is that you have to think on your feet about a problem you haven’t seen before, and the interviewers pay close attention to how you think (out loud) through it. So, having lots of experience coding may give you some advantage, but not a huge one. I found that my years of working on hard math problems, plus a few months of computer science cramming, was a surprisingly good preparation. (Standard Disclaimer: The opinions expressed here are my own, and have not been reviewed or approved by my employer.) The process is far from perfect, and has its own biases, but it minimizes the impact of one’s background and experience. And while not all employers have this type of interview process (though many software companies do), many are willing to overlook lack of experience for the sake of potential.

There are also some things you can do to get more direct experience to put on your resume. Many companies are starting to offer internships, even for PhD students (including Google). You can get programming experience by contributing to an open source project. To show off you data science skills, you can compete in a Kaggle competition. (Those are the ones I know of – if you know of any that I missed, leave a comment below!)

OK, so once you’ve worked out all your non-academic skills, and written your resume accordingly, the final step is to determine how you will avoid setting off red flags that some employers look for from academics. In general, the folks who review your resume will have no doubt that you’re smart based on your math background, but they will also be acutely aware that there’s such a thing as being too smart for your (or their) own good. Many employers feel that hiring a “bad” job candidate is much more costly than not hiring a half dozen “good” candidates, so they spend a lot of energy looking for red flags. When considering an academic, there are certain red flags that they may think they see even if you didn’t do anything to indicate them. So, it’s not enough to avoid the red flags – you need to actively provide a counter narrative.

Here are the things I know of that employers may be expecting you to say, and that they may hear even if you don’t say them: (Did I miss any?)

  1. I couldn’t make it as an academic, so I’ll settle for a private sector job, but I don’t have to like it.
  2. I just want to think about fun, abstract problems, whether or not they’re useful.
  3. This job is going to be much easier than being a professor, so I won’t have to work very hard.
  4. I’m clearly smarter than everyone who currently works for you so I’ll just tell them all what to do.

Now, I know you wouldn’t actually think, let alone say, any of these things. (You wouldn’t, right?) But better than not saying them is to say things that completely refute them (and you have to mean it when you say it!) Have a solid explanation for why you want to leave academia, which focuses on the positive aspects of the private sector. (See my previous post.) Talk about how you want to work on things that have a real world impact, how you’re looking forward to the challenge of adapting to a completely different environment, and how you’ll enjoy being a member of a team and learning from your much more experienced colleagues.

In the end, the process of getting a non-academic job can be long and complex. At the beginning, it feels completely hopeless, but the more you learn and the more non-academics you talk to, the better it gets. And here’s the kicker: There are a lot of jobs out there where the supply and demand dynamics are completely the opposite of academia – where employers are desperately seeking qualified applicants. Once you find your way there, and see what it’s like applying for a job where you’re NOT one of 500 applicants for a single position, it’s completely worth it.


  1. It’s very helpful to hear from someone who has successfully made this transition. As someone who is planning on going for a data science job after graduating next spring, I’m all to familiar with the uncertainties involved in the transition… It’s great to read about your thought process and the advice you’ve drawn from your experience. Thanks for the posts!

    One thing I’d add: You’ve got to be very good at explaining the work you’ve been doing in academia to potential employers, even if what you’ve been doing doesn’t directly relate to the position for which you’re being considered. If you cannot give an explanation of what you’ve done that makes the interviewer feel like they understand your work, they worry that either you’re a bad communicator, you couldn’t make it in academia because you didn’t do much (#1 in the above list list), or you don’t think they’re capable of understanding what you’ve been doing (similar to #4 in the list).

    Comment by Colin Grove — October 2, 2015 @ 8:24 pm | Reply

  2. […] hopefulness really buoyed me forward, as did reading Jesse’s second blog post on this topic.  Here’s an excerpt again contrasting mathademia with […]

    Pingback by The non-academic job search (Part 0)-Deciding to leave | Baking and Math — October 26, 2016 @ 1:41 pm | Reply

  3. Если ты не инвестировал сюда,значит тебе не нужны деньги https://goo.gl/r5osdp

    Comment by Davidadoca — February 15, 2017 @ 5:57 am | Reply

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