Ask HN: Can refreshing my advanced math background turn into a related job?

I went straight into industry with an undergraduate math degree. My success was mixed results. One on hand, it was easy enough to find math pedagogy jobs that did not pay very well. On the other hand, it was easier finding data science / machine learning jobs that technically require and advertise lots of math but in reality are extremely dry logistics.

I have found some success in finding nonconventional programming jobs that have job titles seemingly generated by a bot where free-thinking and drawing up imaginative approaches is appreciated.

Math is everywhere, and every programmer could stand to learn or appreciate more of it so I say to you best of luck in your endeavors but be flexible, since almost nobody knows where they really need math until you explain it to them. A lot of the time math has served me the most because I've already seen a problem or its variants and I know a solution exists.

If you like math, study math. But I don't think refreshing your math skills is the best return on investment if your goal is job hunting.

If you're looking for an ML job, the bar is mostly set by coding skillset and ML knowledge, which is a narrow area of math compared to what you might cover in a graduate math program. That said, it is important to be comfortable with the math that's relevant to ML.

Without direction you could spend a lot of time learning things that aren't going to help in your job search.

My impression of ML has been that there are two types of jobs: one, the most common, where you're basically plugging values into tools like scikit-learn or TensorFlow and spending most of your day-to-day fussing around with data wrangling and general software development; and two, the heavy math jobs involving integral signs and Greek letters, which are much rarer and done mainly by people with top-tier PhDs at research labs.

Since at my age going for a Stanford PhD isn't an option, you probably mean by a better return on investment that I should dig into scikit-learn, Hadoop, the current AWS/Google Cloud/Azure options, that sort of thing. Is that right? Which, that's definitely sensible but not exactly what I was hoping for, since it's basically the same as the regular programming I've done, just with a different set of libraries. On the other hand it makes some use of my math background, and perhaps the dream of reliving grad school at a paid job isn't totally realistic anyway.

I've got the math PhD, and the job that requires a mix of math & programming. Living the dream, right? Except, my employer kinda stinks (and it's a startup, so our future is far from certain), so I've always got an eye on job listings. Only, I'm not in ML so the only real openings that I see that I'm "generically" qualified for are in quantitative finance. I don't like cocaine, so that one's out too (sorry for the broad brush, quants, but I've met a few too many coked-up brogrammers in your field). If all I had was a bachelors, maybe I'd go for actuarial work, but that would mean ditching my programming skills.

I'm older, and stuck living in an expensive metropolitan area due to family stuff. I feel stuck in a job that I'm not satisfied with. I expected that having a PhD would be a boon, but what I'm finding is that it's hard to find a good job that actually depends on the PhD -- it feels like an albatross, but I can't exactly hide it because that would be a decade-wide hole in my employment record.

The number of technical jobs that actually require a math PhD must be relatively small. I'm on the other end of it, trying to avoid the situation where the degree type is meaningless, because this means that, as far as credentials go, you're competing with anyone with a college degree. I suspect in your case you have more room to maneuver than you think, but what do I know? Good luck to you.

> The number of technical jobs that actually require a math PhD must be relatively small.

Yeah, that's my point: if you're looking for a boost to your credentials, my experience is that a PhD in math won't really open that many doors. By all means, pursue a higher degree for the sake of your personal enrichment! But a word of caution does seem necessary if this is a career-oriented choice.

I know several very successful actuaries who also write a fair bit of advanced code. Insurance companies are always looking for quantitative improvements and it is far more expedient when the actuary doesn't have to explain (in excruciating detail) the requirements to a programmer without the same math chops.

You can likely land a pretty decent job as it is. People with advanced math degrees who can also program are in demand. Or at least that was the case pre-virus. If you want to take it easier and apply for a job when you feel ready, that's a fine choice too - you will do as well as anyone can in the current circumstances.

As someone who also possesses an unused advanced maths degree, I would suggest pursuing mathematics for the sheer fun of it first and then decide in a year if you really want to be involved in statistical modeling, ML, deep learning or whatever.

You may end up deciding that being an insurance actuary is the right challenge: there are multiple exams to pass involving a lot of maths and the pay is quite good.

For recreational maths I’d recommend rereading Polya and playing with “Crossing the River with Dogs” and “Math Recess”.

In financial capitals, actuaries are amongst the top earners on par with lawyers.

> I bogged down in the second chapter or so. I know if I really focused, I could get through it. But would anyone care?

Yes they would. But you have to be able to use it to solve real problems.

Alternatively, you could be a dev who supports a science team.

Do you know if the typical ML hiring process is able to distinguish between someone at the MOOC/"Machine Learning with [current popular language/library]" level from someone who's done hundreds of problems in a graduate-level math textbook? I'd love to be convinced because as I said I'd like the excuse to work through the book, but at the same time, as has been suggested elsewhere, there's more practical stuff to focus on too.

Well, obviously it depends, people can be successful from both camps based on other attributes. But I will say when we interview we often try to weed out people who just took a handful of courses that focus on TensorFlow, but lack general science intuition and depth.

...On the other hand, if you're already a dev and want to become an ML Dev, knowing how to do science deployments, work with big data, and familiarity with APIs like TF would be more valuable than knowing how to do proofs.

I suppose it's like software development generally. To be good at it you need a sense of what good programming feels like, but advanced books like Knuth's "The Art of Computer Programming" aren't really relevant to the day-to-day work of gluing together libraries and writing code that doesn't go deeper than for loops.

Similarly I suppose most ML work is done with some solid basics but advanced math textbooks aren't really needed, and people actually working at the theoretical advanced math level are rare and in a handful of academic positions or corporate research labs requiring high credentials or other specific qualifications.

Thanks for your input.

You may want to act fast. Data science and deep learning are still a bit young, and I can see shops hiring someone with strong math chops. I have some direct experience with this as well (on the hiring side). However, this may not persist for a long time. I saw the same in finance. In the early days, people would hire smart people with math backgrounds for finance roles. After a while, universities started to have specialized Masters degrees in quantitative finance. Speaking to friends, this seemed to have made it harder to break in for someone with just math talent.

Well, it could help you get a job at Subway Sandwich:

https://www.independent.co.uk/news/science/that-figures-prof...

I don't think you can get a job on math skills alone unless they are on a PhD level or higher. A Master's degree in math isn't worth very much, tbh. Bishop's book is really good though. I've also been using it for self-study.

Would you read through that book and generate some material that can prove you did it? Like a project on Github with a nice readme or some blogposts? If you have that knowledge and sell it properly you're grand, you'll get the job.

One of my friends got his PhD in math a couple of years ago. He has since been working for various merchant banks and investment managers as a consultant. I have come across several other math majors in insurance industry as well.