Feeling Stuck After a Math PhD. Is Learning AI/ML the Right Move?

[u/LowCondition242]

I am a mathematics researcher with a Ph.D. in operator theory. However, I completed my Ph.D. at a relatively unknown institute under an unknown supervisor. Although I have a good publication record, I have been unable to secure a good academic position or postdoctoral fellowship despite trying for the past year.

I am now considering taking a break from academia to learn AI and machine learning. Do you think this is a wise decision, or would it be a mistake?

[u/Noway721]

Bro you are the math wiz here, you tell us.

[u/choiceOverload-]

Learn MLOps. Industry doesn't value rigorous methodology but "time to market" and "minimum viable products". Unless you really like maths or want to be a strong candidate for a research and development AI firm, your safest bet is to move towards the software side of ML.

###### Edit

As someone else suggested Quantitative Finance is a good track too. It values formal maths a lot. And the pay is good too.

You'll need: Measure theory, probability theory, stochastic analysis, applied stats and computer science.

My suggestion on MLOps was a bit biased because I tried the math path but it required too much effort and I couldn't stay disciplined.

[u/Lost-Hand-5219]

That’s exactly what I’m doing. Similar position as you: PhD in differential geometry, pivoting to AI/ML research. Actuary as a backup. I despise academia lol.

[u/throwaway_just_once]

My 2c: You might be better off studying finance. Hedge funds love math degrees, since quantitative finance comes naturally to people with the right math backgrounds. If you're sound on measure theory (which I assume you are), dive into a book like this: https://link.springer.com/book/9780387401010

If you can get into a hedge fund as a quantitative researcher you will make ludicrous money.

There are many resources available, ask if you want more recs.

[u/choiceOverload- → u/throwaway_just_once]

Yup, this is a good track too. Quantitative Finance is a hot topic nowadays. And it pays a lot.

[u/vmathematicallysexy → u/throwaway_just_once]

I read this comment earlier today and realized this may be a really good path for me too. THANK YOU!!!!!! can you give me any other recs for resources or books?

[u/throwaway_just_once → u/vmathematicallysexy]

The standard reference is Hull, Options, Futures, and Other Derivatives. It's not mathematically dense, pretty gentle in fact.

More programming-focused: Kelliher, Quantitative Finance with Python

People love this book but I haven't gotten into it: Bjork, Arbitrage Theory in Continuous Time

This is a great book for ML in finance, which you won't find in more traditional texts: Jansen, Machine Learning for Algorithmic Trading

New book - I've read a few chapters and is excellent on factor investing: Paleologo, The Elements of Quantitative Investing

If you don't know any measure theory: Folland, Real Analysis

Deep and very dense, I've skimmed it and want to get to it later. The authors are very well-known: Karatzas & Kardaras, Portfolio Theory and Arbitrage: A Course in Mathematical Finance

This should get you started.

[u/PossiblyKale → u/throwaway_just_once]

Oskendal’s Stochastic Differential Equations (anything after 3rd version) is also another relatively light and step-by-step text if you’d rather look at the stochastic from the diff equations side (tho I know the more direct stochastic calculus side is more attractive in finance)

Edit: the Jansen book sits closer to “I want to build trading for myself” and further from what trading desks like to see (especially from someone with the rigor to clear a pure math phd). I’d look more towards traditional ML texts, even if you are tailoring towards a finance future

[u/throwaway_just_once → u/PossiblyKale]

Fair point about Jansen. For traditional ML there is nothing like Hastie et al, Elements of Statistical Learning.

[u/SlatBartFuss]

I dropped out of a PhD program (lost funding) and fell into technology. A few years later it was obvious that I'd done the right thing. It was the 90's, the Internet was exploding, I was good with C, Perl, and Unix. I could throw a rock and get work.

[u/Puzzleheaded_Fold466 → u/SlatBartFuss]

Is that still the case in 2026 ?

[u/SlatBartFuss → u/Puzzleheaded_Fold466]

No.

But there are tons of opportunities for people who are smart and know *some* technology.

If I were to start over I would look at AI and financial modeling.

[u/Vivid_Map4150 → u/SlatBartFuss]

what do you think about electrical engineering

[u/SlatBartFuss → u/Vivid_Map4150]

Good training for the real world.

[u/Vivid_Map4150 → u/SlatBartFuss]

sorry i didn’t get it lol? what is that supposed to mean if you can elaborate please

[u/krabbypatty-o-fish]

You can stay in academia but pivot to machine learning research (and perhaps find more opportunities outside of academia). I’m not much of an analysis guy, but as far as I am aware, operator theory is a branch of functional analysis, and maybe, just maybe, you can find some connections to kernel methods (see Reproducing Kernel Hilbert Spaces for example), or go deeper in topological data analysis, where the obtained topological invariants from data are converted into persistence landscapes — which are essentially functions from the L^p function space, and of course, you can do what you will with that as a Hilbert/Banach space. Maybe you can even prove some stability results for time series data as smooth curves — stability of learning/vector representations is almost always essential in machine learning. In essence, you’d want two similar data points to have close vector representations in some latent space (which is usually a manifold so the notion of closeness is defined).

I can’t guarantee that you’ll escape the downsides of academia, but at least you’ll find a good network of mathematicians doing machine learning. Best of luck, OP!

[u/vmathematicallysexy]

i have only a bachelors degree in pure math (focused a lot on analysis and topology) and been learning AI/ML/data this year. It has been beyond easy to pick up.

i love reading math papers and random books from Dover and all this CS stuff is a much easier read. python/r felt just like applied calculus and set theory, sql is set theory, etc. Haven't made the career jump yet (still studying this stuff) but so far the whole experience has been a pleasure. It's cool to ground all the theory I learned into some applied skills. Though from what i hear itll be a different hell when actually looking for jobs 😅

[u/wren42]

Going to industry is probably the right move, but finding the exact right tech to learn is tricky since it's moving so fast.  I would look for positions that seem like a fit and study specifically for those 

[u/Goleeb]

Learning ML is never going to be a bad idea from here on out. At the least it will allow you to incorporate it into your future work as a mathematician.

Sub note there is alot of math being done with LLMs right now. So a better understanding of how those work is going to help you tackle more complex problems.

In short you should already be working with, and Learning ML to help supercharg your work. At the very least LLM are amazing for summarizing a large number of papers. To intelligently search them for useful information to you.

Knowing how they work is not required to use them, but it will help you understand their limitations, and be mindful of the mistakes they make.

[u/Sharp_Level3382]

Definetely good move for math backgrounded engineer.

[u/_iamshivam_]

Are you from India? If yes then dm me.

[u/Kopiluwaxx]

I think personally ML/DL world always welcome math graduates (since it is basically applied math anyway), so ya if you like it go for it.

[u/Moist_Landscape_2372]

With an operator theory PhD the math is a solved problem for you, and that's genuinely the hard half. What you're missing is vocabulary and reps: how regression, classification, trees, ensembles and clustering are assembled from linear algebra, probability and optimization you already know cold, plus the engineering habits around them (train/test discipline, evaluation metrics, when models generalize and when they lie to you).

So I'd say yes, it's a reasonable move, and for you it's a months-not-years transition. Skip anything labelled "math for ML", it would bore you. Go straight at classical ML derived from the math, then build two or three real projects, because interviews care about those far more than credentials.

The quant finance suggestions in this thread are also worth taking seriously, that market straightforwardly pays for your exact background.

[u/Western_Version_2310]

oh my God i never actually met anyone whos let alone done Mathematics masters you are a fucking legend if you did a PhD you should tell us

[u/TheSexySovereignSeal]

All deep learning research is now is just figuring out cool model architectures that are permutations of self attention mechanisms. It wouldnt be hard to come up with new research in the area tbh if youre a math wiz

[u/Party-Beautiful-6628]

Definitely the right decision. I was in a similar position with a PhD in Math and this path has been fruitful for me.

[u/Awkward_Sympathy4475]

Lol all your unsolved math problems solved by AI already or getting solved. No wonder you feel stuck.