this is an "almost" Ph.D. in pure maths who's got a few reasons to look for a job in the industry.
I know that many choices are out there for a math guy: finance, consulting, industrial research, big data analysis, machine learning…
My main problem is that I have a very theoretical background, mainly in metric and geometric topology. In the last years, I've been decently active in learning a few programming languages (mainly C++ and Python) and some fundamentals in both supervised and unsupervised machine learning.
I would love to find a job in this interesting area, and I believe it is a sector which is quickly expanding, given its many applications. Research jobs in the industry would be appealing too, but I believe they would require a bigger amount of knowledge in probability and partial differential equations, which I do not know.
Any job which makes use of maths in a creative and stimulating way, to solve real-world problems, would be appreciated.
Looking for job ads, I realised there's not much choice for people who have such a theoretical background as me. I would be interested in any advice regarding how to switch from a pure maths position to an applied one. Unfortunately, I can't go through more courses, as I need some financial support and I could not afford paying for an additional master or any other specific course in applied maths.
Ideally, I guess I'm looking for a company which is willing to allow its employees some period of training, before they start with the actual work.
I'm currently based in the UK, but I wouldn't mind moving to a different european country.
Thank you in advance for any recommendations you might suggest!
Best Answer
I disappointed a number of interviewers when I had to explain that model theory was not much to do with mathematical modelling...
I suspect you overestimate the amount of maths needed for quantitative work in industry. You have had a training in structural understanding of hard numerical or abstract problems, and this is valuable in itself. Quantitative people are rare.
For me it was easy to learn on the job all the statistics needed for work as a hedge fund analyst: really an introductory textbook on econometrics covered everything general, and for specifics the tools used by the firm were in front of me. In effect you are given a set of levers, and it's quick to discover what they do. Basic reading on the efficient market hypothesis, say, would have done me far more good at interview and for the first months of the job than boning up on Ito calculus.
So you really needn't hamstring yourself by saying you'll need training before you can start.
Further, I'd suggest it's a mistake to be hung up on "using maths". There are real-world problems that you, as a mathematician, are able to get to grips with better than everybody else. Real problems are worth solving in their own right (especially if you find more socially valuable work than the financial industry) and it's this that gives them interest, rather than the particular tools you use to crack them.