Physics grad student: how to build employability in programming & finance

Good at math, definitely not "gifted".

Don't think like this. You are an expert at quantum computing. 99% of the population doesn't know what quantum computing IS. You are probably 3-4 sigmas above the mean, which basically qualifies you to do anything. If you don't think of yourself as particularly smart, it is probably just because you are in contact with people who are 5-6 sigmas above the mean.

Physics PhD who just got a job in industry. While I can't tell you the exact thought processes of my interviewers, I got the impression they were impressed by my being the lead developer and architect of a library. You may not have the opportunity to build something that can be posted on the web, but if you see any such option you should grab it. Failing that, you could try to make some contributions to an open-source project (there are any number of them on GitHub), or to start building a small game. In short, try to build up a portfolio that you can publicly point at and say "I built that".

Additionally, there seems to be a somewhat standard set of algorithms and big-O analysis questions that interviewers return to; you could do a lot worse than just reviewing some basic comp-sci cheat sheets. This also means that practice makes perfect on interviews - it is not the case that each one is a unique gem of highly specific knowledge. So you could even just apply for some jobs right now, go to the interviews with no expectation or intention of being hired, and see what sort of thing you should learn.

Write code. Lots of it. Becoming a good programmer is the same as becoming a good anything: it is mainly a matter of practice. You need to get those hours in now.

You also need to choose a field that you are interested in, so that you will be able to maintain the effort required to succeed. But I would recommend weighting your choices towards fields which give you an advantage towards employment given your situation. Machine learning, for example, would be an opportune choice, given the similarity of subject matter (statistical analysis), and the fact that there are large H1-B employers with demand for Ph.D grads with machine learning experience (e.g. Google).

Pick a sub-field, and learn it inside and out. And code, code, code.

Source: I'm was undergraduate physics student that went on to write code at NASA and now does full-time bitcoin consulting.

Comp sci maths should be piss-easy for a Ph.D physicist. Being able to sling this stuff about in your head will definitely give you a leg up. Find a textbook to swallow. (SICP, as recommended below, is a good one.)

Some generic advice.

Imagine yourself in the role of your potential employer (e.g. a hedge fund manager). Which uncommon skills would you be looking for in applicants?

Re programming -- have a project. Have multiple projects. Throw code onto GitHub. Demonstrate your ability to construct and debug a working piece of software that does something useful. If you want to be employed as a coder, the first question will be "Show me what you did".

Re finance -- acquire experience with real financial markets (as in, not models from academic literature). Pick an area, learn it. Paper trade or even better, live trade with whatever amount of money you can afford to lose. Figure out market frictions, figure out risk management. Get acquainted with all the ways in which real markets are different from theoretical ones. Get your hands dirty.

I'm also a physics grad student (experimental high energy) who is considering industry jobs in addition to postdocs. I've attended several career panels in the past few years. Most recently, a panel was held at Fermilab. One of the panelists started a blog, Science Jobs Headquarters, where you can read about that panel and get other good advice.

A few of my takeaways from the panel: 1) Python is really useful and everyone should learn it. (I need to work on taking this advice. I mostly develop in C++, and my Python is patchy.) 2) Some companies want to hire people with very specific skills and experience, but other companies are just looking for smart people who can learn on the job. The important point here is that few skills are absolutely essential to get a job in data science/private research/consulting/etc. Even if you're not a Python whiz, there are still people looking to hire you. 3) The website Glassdoor was recommended for investigating companies at which you might want to work.

Biophysics grad student dropout/work at startup now; I personally was sort of sick of mathematical modelling, so I decided not to go the machine learning route. But as I was based in Boston, there were machine learning jobs everywhere. I enjoyed working through SICP, and its functional style been pretty useful in quickly understanding new concepts (Javascript callbacks and promises, for example) in programming.

I got my programming start in a website for a tournament that I ran - it taught me my way around a large framework (Django) and as django was what people call a 'highly opinionated and bloated' framework, it taught me one version of how experts think a large-scale project 'should' be organized.

If you read one book on the computer science side, read this: http://mitpress.mit.edu/sicp/. All of computer science is in this book. Not a simple book at all.

Hacker School is totally free (with living expenses paid if you're a woman). I believe five rationalists including myself have done it. Unlike most bootcamps it has basically no official structure--you and all the other hackers/aspiring hackers think of cool stuff to do and then do it. They will help you become a better programmer and find a job. The community is great.

I don't know specific techniques to design good algorithms for problems.

I would suggest reading an introductory book on algorithms and data structures. There are a number of good ones, and none of them is strictly better than the rest, but for your case I would recommend Steve Skiena's Algorithm Design Manual, which can probably be found in your university library. It's very readable, discusses how to go about solving algorithmic problems, and has a lot of breadth.

This is some of the higher bang-for-the-buck knowledge in CS, and surprisingly relevant to the Real World.

Artificial Intelligence: A Modern Approach, in addition be being a smashing good book on AI, include many projects small enough for novice programmers to complete, yet compelling enough to feel proud of completing.

My reply for building up a broad programming competence from the last time this was asked. I'm a professional C++ programmer, but don't know as much about the business side of software business as I probably should, so if your terminal value is a strong career trajectory instead of a good programming competency, there are probably ways to optimize the list.

I'm in a very similar position to the poster (same field of study, same time line) and am very interested in the answer, just posting to signal that (at least) two people are looking for this advice.

Find a python program that does almost what you want and hack it to do what you want in a way that's maintainable, and then contribute that back. Do that often enough with enough projects that you have a portfolio of both small bugfixes and projects that you're moderately involved in.

Once you have a good portfolio, I'd try getting an internship at a software company. Most internships either come with job offers or are near-perfectly correlated with job offers, so treat this seriously.

I'd focus more on real-world coding than working through CS textbooks. Not a lot of academic computer science is really relevant to a career in the software industry. Most of SICP definitely isn't. SICP is a great book, but it's very academic.

See also: http://matt.might.net/articles/what-cs-majors-should-know/

Stack overflow careers may be of use to you.

Here's a list of coding bootcamps.

I went to App Academy. Before going to App Academy, I had taken two college classes in comp-sci/programming and tried to teach myself some web programming. I think the autodidact route has a major draw-back which is that it's easy to get really stuck, and that can be very discouraging.

Not only is App Academy really good for learning, but you don't have to pay them unless you get a tech job afterwards.

Also, there are quite a few other LWers who've gone to app academy.

Good luck!

You could also supplement your learning by doing a couple of standard industry certifications related to security, although I do not know how relevant something like CISSP would be in quant finance environments, it would give you a little bit of background, especially if you haven't been interested previously. This and other similar courses also give you a considerable foot in the door at certain environments requiring security clearances.

I agree with the rest of the comments that a github profile (even contributing to interesting projects you didn't create yourself) outclasses a CV, I'll post a video by Continuum Analytics about why python is getting so strong in science in analytics, but can't seem to find right search criteria, right now.