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Transitioning into Quant Roles from Enterprise Risk

Hi All.

To give a bit of background - I have a bachelor's degree in engineering and I recently completed a master's degree in computational fluid dynamics where I wrote a thesis on designing and optimizing new numerical non-linear PDE discretizations (energy/entropy-stable methods). During my master's I coded mostly in MATLAB, FORTRAN, bash, and some C++.

After I graduated I obtained a position in enterprise risk for a large bank in Toronto, Canada doing model implementation for regulatory stress-testing credit risk models i.e. estimating PD, LGD, EAD (been here for about 10 months). This doesn't intersect with the front-office at all and is primarily about the bank's loan/credit portfolios - I spend most of my day doing model testing, UAT, coding in SAS and bash, ad-hoc stress-testing runs with our platform, investigating bugs, looking through the models and communicating with model development. Most of the models are statistical models: linear/nonlinear regression, Markov chains, Gaussian copulas, hazard models etc.

I'd like to transition to a front-office quant role in capital markets or market risk at one of the large Canadian banks (my current bank for example) doing model development. In my current role I found I'm more interested in something which is a bit more mathematical and related to my Master's research. I've already found some groups in my bank that are doing exactly what I want to do i.e. developing models for pricing exotics, IR derivatives, XVA etc. or working with these groups to develop risk models. However, I realize that I'm lacking some skills (mostly in stochastic calculus) so have the following tentative plan for the next year:
  1. Refresh linear algebra (since I've forgotten a lot since undergrad and only used basic numerical linear algebra during my master's research)
  2. Refresh probability: Introduction to Probability by Blitzstein and Hwang
  3. Financial Calculus by Baxter and Rennie
  4. Stochastic Calculus for Finance II by Shreve
  5. Code up some derivative pricing models in C++/Python
I was wondering if anyone any suggestions about how to make myself competitive for these types of roles or any other things I should be working on? I don't really have any interest in getting an MFE degree since they're extremely expensive and I feel confident that I can learn most of these things on my own. If anyone currently works in Toronto in a quant role I'd be happy to hear from you.

Thanks.
 
Hello, I have a similar background as yours.
How did you manage to get a position in enterprise risk for a large bank, straight after graduating?
I'd say you have already more than a foot in the door: to go further, try to find a way to network with the cva people in your bank. They will be able to assess what you can do now and what you need to do in order to land a position at their office, when is made available.

Out of your study list, I would switch linear algebra for a course on algorithms coded in C++.
 
My guess would be knowing computational fluid dynamics and Fortran would make an impression.
Fortran conjures up the idea of punch cards and non-OOP programs of 10 thousand lines of code...
CFD and Fortran go together, too much legacy code and Fortran is damn good at what it does.
A part of course the happy few that use C++ with OpenFoam. There is also people who use Python, but I think they just want to get noticed.
 
Have you considered the ARPM bootcamp from Meucci? Given your background, that seems the best bang for your buck. You'll encounter many of the same concepts, but filtered with respect to finance application. I did the bootcamp, which was phenomenal. I was not nearly well prepared enough to take advantage, but it did provide me with a map of the unknown territory. I'd really like to do the marathon online myself, even as I'm wrapping up the final stages of grad school. I worry w/ the probability books, you're going to spend a lot of time on irrelevant problems. Shreve's text is fantastic, though.
 
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