Is there any way to get experience in the quant field without being qualified for a position?

Develop a skill that quants need, but don't necessarily have. In the 1980s, I became pretty handy with the IBM PC and PC/AT (NB: 640k RAM and a 10 MB hard disk). The quants at my company were all VAX and PDP11 folk. When they saw how easily I could manipulate datasets, they asked me to join their team, where I was the designated grunt for six years. I probably ran 250,000 regressions. It set me up for grad school and the rest of my career.

To be sure, times are different now, but I suspect quant teams of junior people to set up the analyses.
 
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Hello guys,

Sorry if this is a bit long!

I currently studying Electrical and Electronic Engineering at a semi-target UK Uni (think Warwick, Manchester, Durham), entering 3rd year, predicted a first class with first class grades in classes like Linear Algebra, Vector Calculus, Differential Equations, Signal Processing and C/C++, and will be taking classes in PDEs and Numerical Analysis. I will also have a ML based 3rd year project (proposed project is on using time series analysis to develop a means reverting strategy for pairs trading so will get more experience). Furthermore I just finished a SWE internship at a top tier investment bank (think GS, JP or MS), with a return offer for another internship.

I am interested in quant finance, as I believe I have the ability and desire to further my maths skills, as well as develop my programming skills and learn more about the markets. I had some exposure to the markets during my internship and I found it absolutely thrilling. The problem is, the best of the best master's courses (i.e. Maths and Finance @ Imperial or MCF at Oxford and basically all the Maths x Finance Masters) only explicitly take maths students, and I'm not sure I'd want to waste my time applying to them as I am almost certain I'll get rejected. I am therefore looking at the following courses that I think have the right blend of math, finance and CS/ML classes (I'll list them in order of my preference):

1. MSc Computational Finance @ UCL
2. MSc Financial Technology/MSc Risk Management and Financial Engineering @ imperial
3. MSc Financial Technology @ UCL (bear in mind the FT and CF courses share almost entirely different core modules, but have more or less the same optional ones)
4. MSc Advanced Computing @ Imperial (I like this because I can choose all my modules, and they are good if I decide to go down the more Quant dev/Algo dev route which is something I am considering, given my background)
5. MSc Computational Finance @ KCL
I am also considering MSc Computational Mathematical Finance at Edinburgh and (if all else fails), perhaps MSc Quantitative Finance from Manchester.

What courses would you recommend I add/remove from there? I'm quite keen on the first 2 and the 4th one, first choice being the UCL CompFin due to the compulsory placement.
Imperial is where the indecision is, my slight preference is FinTech because (in terms of core modules) they still have a good number of maths/stats classes, but perhaps more application and more general skills that would serve me well if I decide to go start my own company for example. I also really like the Financial Econometrics in R/Python module, which isn't on the RMFE course. My only concern is whether Imperial FinTech won’t look as good for quant on my CV/LinkedIn as RMFE, as the mathematical rigour is definitely there, but not as much as RMFE. Then again, my concern with RMFE is that it seems pretty specialist and I might narrow myself down to more middle office quant/risk management positions. I'm stuck here!

Would love to get some feedback!

Thanks a lot!
 
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