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Suitable roles for a PhD graduate (in computational fluid dynamics)

Can I become a buy side quant researcher with the following background: final year PhD skilled in finite element methods, C++/CUDA/Python and algorithm development?

These skills are used for pricing models [thread 1, thread 2], but my impression is that pricing is a decaying field.

Instead, this post says:

"... the vast majority of buy side quants even at options trading firms will not work on the options pricer itself but focus more on market making algorithms, signal analysis, predictions ...".
  1. Would a buy side firm be willing to take me on and let me learn on the job?
  2. If I do a data science project, would this be evidence of sufficient statistics knowledge?
  3. Can I join a buy side firm as a quant developer, learn statistics in my own time and then move to quant research?
Edit 2: Dear mod, I am really sorry about the edits but I think my original post was too dense.
 
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I think your profile would be considered for graduate quant research roles. Most Ph.D. graduates are not hired for their specific research experience but their demonstrated ability to work relatively independently on complex problems. You bring some relevant experience related to numerics and while indeed not the focus at most buy-side places, it is a positive selling point. Your interviews will likely still focus on probability and statistics and would recommend brushing up on these a bit. I would not apply for quant developer roles initially (as long as you didn't exhaust your quant research options). While there is some mobility, I have not seen it very often. Also, while the role is called "quant" developer, you will not necessarily work on quantitatively interesting topics.
 
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Thanks for taking the time to respond. Your reply is encouraging for me, although I have a simple follow up question about one of your points:
Your interviews will likely still focus on probability and statistics and would recommend brushing up on these a bit.
Would you be able to tell me what level of probability and statistics is expected from me at interviews?

For example, would it suffice if I were able to comfortably answer all the questions from Chapter 3 of this document? Or would I have to be able to explain the maths behind machine learning models to a decent level, for instance?

If you could tell me this it would be very helpful: I can begin interview prep right now if I don't need to know the maths behind ML models. If I do need to know the maths, then I have a lot more left to study.
 
If you could tell me this it would be very helpful: I can begin interview prep right now if I don't need to know the maths behind ML models. If I do need to know the maths, then I have a lot more left to study.
This will depend on the role. For general options quant research roles, the questions in the chapter you reference are close to what you typically see in interviews. If you apply for a more specialized role involving machine learning, then you can expect questions on this too.
 
I see. I'm assuming that the specialised roles involving machine learning are the most interesting/well-paid quant research roles at the moment. In that case I will have to take an ML course and do a personal project to pick up some of the necessary skills?
 
I see. I'm assuming that the specialised roles involving machine learning are the most interesting/well-paid quant research roles at the moment. In that case I will have to take an ML course and do a personal project to pick up some of the necessary skills?
I don’t see this being the case, no. The best-paid roles are typically the ones closest to trading. I personally use very little machine learning on a day-to-day basis. Especially in options trading, people care about the interpretability of their signals and position. The edge is often in designing good features and predictors as opposed to using complex models.
 
I don’t see this being the case, no. The best-paid roles are typically the ones closest to trading. I personally use very little machine learning on a day-to-day basis. Especially in options trading, people care about the interpretability of their signals and position. The edge is often in designing good features and predictors as opposed to using complex models.
I understand. Thanks a lot for your advice, you may see me post again on this forum with more questions as I get deeper into the application process. Hope you have an alright weekend :))
 

Daniel Duffy

C++ author, trainer
It's not every day one comes across someone with FEM and computational fluid dynamics. It was an area I worked in both in academia and industry It turns out that my PhD research in 1980 could be direct;ly applied to PDE in finance. I have written up here for FDM.


(no FEM in book because no one knows it??)
BTW was your FEM blue sky stuff or working on existiing FEM packages?

With your background, it should be possible to make an impression based on C++ and maths knowledge.
And if your underlying numerical analysis is sound enough then ML and data science (whetever that is..) should be doable.

My 2 cents.
 
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It's not every day one comes across someone with FEM and computational fluid dynamics.
To be honest, it's the Discontinuous Galerkin method, which is only "technically" FEM: practically speaking it's FVM but using linear instead of flat shape functions.
BTW was your FEM blue sky stuff or working on existiing FEM packages?
I wrote all my code from scratch and my PhD is about parallelising this model on a GPU.

To make the parallelisation possible, I implemented a new algorithm and a new data structure and made adaptive mesh refinement fully resident on the GPU (no CPU/GPU data transfers), which is the crux of my research.

My research is sadly not about mathematical innovation but I do like maths though I've never taken a proofs-based course.
With your background, it should be possible to make an impression based on C++ and maths knowledge.
I don't know if linking to one's GitHub is allowed on this forum but mods, please let me know if not.

This and this are examples of the C++ code I've written, though unfortunately I can't share the main, more complicated C++/CUDA code I have developed until I'm published.
And if your underlying numerical analysis is sound enough then ML and data science (whetever that is..) should be doable.
I can read and follow a numerical methods paper, but I have no formal training in numerical analysis (or maths in general). Do you think it's worthwhile for me to do some proof-based study of numerical analysis? If so, have you got a learning path? I may start with real analysis.
 
@Daniel Duffy I noticed that you liked my message - does this mean you think that I should do some proof-based study of maths? Or any other comments. Thanks.
 
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Daniel Duffy

C++ author, trainer
Hi alovya,
I was planning to writing up a small response offline indeed. I am not in IB or a headhunter but my focus is on learning important stuff in maths, C++,. Python. My online courses are on www.datasim.nl and I do much of everything. Have a look at your convenience and let me know if you have any queries. regards D
 
Hi alovya,
I was planning to writing up a small response offline indeed. I am not in IB or a headhunter but my focus is on learning important stuff in maths, C++,. Python. My online courses are on www.datasim.nl and I do much of everything. Have a look at your convenience and let me know if you have any queries. regards D
Great, I will take a look at your courses, which do indeed look like much of everything. If you had any other thoughts e.g. about my research or coding stuff, I would be happy to hear them.
 

Daniel Duffy

C++ author, trainer
Thanks. I started maths in 1971 LOL. I got my 1st FEM course in 1974.

I would say major blocks in my Pure maths course (analysis) are vital.

Regarding numerical analysis, it is not necessary to know the theoretical stuff but more a good hands-on feeliing on how to use the methods and after that reading more into the stuff. Then it starts getting closer to ML.

I am just wondering if all these high-potential data scientists understand the underlyng maths.


If you want to dig deeper then this maybe

 
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