[returningfory2]

> Because of ethical reasons I want to stay as far away as possible from anything that is insurance, finance or crypto related. I'm not really interested in AI and/or machine learning either.

I think you might be internally overestimating how employable you are right now. Getting a first industry position after doing pure math is pretty hard. I don't think you quite have the luxury of a priori ruling out broad swaths of industry. Especially fields (machine learning and quantitative finance) for which folks with strong pure math fundamentals are particularly well suited.

You mention "low level stuff and assembly" and I think it is true with the right preparation you can break in here. But keep in mind that when you apply for systems programming jobs, say, you will be competing with people who have degrees and internships and potentially experience in the field specifically. A math PhD is good in theory, but by itself it has lower value than more targeted education and internships.

I'm writing this because when I did my math PhD, my peers and I had a lot of hubris around this issue. The conventional wisdom was that if academia didn't work out it would be easy to jump into industry. Then when the time came around it was an order of magnitude harder than we thought. I know multiple people who took temporary 1-year academic positions because their initial industry job search fell completely flat and they needed more time to prepare. In retrospect being more humble about our prospects and preparing correspondingly would have helped.

[numbers_guy]

Both machine learning and quant. finance are extremely extremely competitive. Having a math Ph.d does not cut it. It is mostly irrelevant actually. For quant. finance, if you have top grades from a highly respected university, that might get you an interview, but I think to do well, you'd have to do months of preparation.

Machine learning is my field and I can tell you, nobody is desperate to hire some math Ph.d who never did anything with ML.

[antegamisou]

> Having a math Ph.d does not cut it. It is mostly irrelevant actually.

Maybe this applies to the lower paying quant roles (Trader/Dev).

No chance of getting hired for a Quant Research position without grad school education though.

[returningfory2]

I fully agree. A math PhD without any practical skills (programming, ML, etc.) isn't that useful for the jobs market. When I mentioned hubris is my parent comment, I was kind of referring to this: the sense among PhD students was that we could easily jump into industry because a math PhD is so valuable. But this was very, very wrong.

[ginnungagap]

> The conventional wisdom was that if academia didn't work out it would be easy to jump into industry.

Thanks for your comment, this is definitely a widespread opinion in my department as well, but I've heard from many people who went into industry that it wasn't quite that easy. This is part of why I'm asking now that I have a long time to prepare

[returningfory2]

I remember when searching for jobs in the last year of my PhD I was confused about why it was going nowhere even though everyone had told me it would be easy. I then had this huge realization that the only people who had said it would be easy were still in academia and had never tried! And I also remembered that _I_ had sometimes told people that getting an industry job would be easy! I guess it's just a weird thing in math departments that this kind of idea gets thrown around without any kind of validation.

Good that you've realized this early though.

[red_trumpet]

Here is my theory: From the point of view of a math department, most people who did a PhD and don't end up in academia finally have a well-paid job somewhere in the industry. Whereas there are many stories of people who didn't make it to a permanent position in academia, even if they tried.

That doesn't mean that finding a job in the industry is easy, so taking time to prepare for it is certainly good!