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Total Posts: 11
Joined: Jun 2018
Posted: 2018-07-13 00:31
I read this somewhere:
"If your goal is to find arbitrage opportunities, MS in CS is more relevant. Most arbitrage traders in equities, futures, or FX do not need sophisticated math to find trading opportunities. However, if your goal is to work in risk management, exotic derivatives, or fixed income, a MS in Applied Math may be more relevant.
In general:
Quant Researchers = Mastery of Math with basic programming skills for prototyping and statistical research.
Quant Developers = Mastery of Programming with basic working knowledge of quant finance math."

Is the above true? How is the market for those 2 highly specialised professions? Should i follow the career path to a specific one or just follow the one that im good at (or passionate) and hope for the best? I wonder if Machine Learning and AI will replace the researchers/analysts or developers...

Thank you in advance!!!


Total Posts: 4
Joined: Jul 2018
Posted: 2018-07-31 06:51
What you've quoted is true.

As for what the job titles mean, it usually varies across firm and breaks down once you've started working. You should just read the job description and speak to the recruiters to find out more. Firms often pick the 2 terms to imply a tradeoff between time spent, skill requirement and experience in research vs software development. Quant developers usually spend more time building something to spec and implementing tools that support the strategy development process rather than doing the actual idea generation itself. Often they'll have to be more rigorous in their coding practices than the researchers. Vice versa, researchers have to be more rigorous in how they test hypotheses.


My career advice for people still pursuing their university/college education is as follows:

1. You can become wealthy being at the top of almost anything you do and being happy and passionate about your work. Politics, writing, swimming 100m, baking, quant trading, FX derivative arbs in G10s.

2. Don't pay too much attention to the current job climate if you're 2-4 years away from graduating. 2-4 years is a lot of time in tech and finance. Whatever's most financially lucrative now is unlikely to be most financially lucrative 2-4 years from now. It's obviously not easy to forecast what jobs are most lucrative 3 years from now, as with all activities that one can become extremely wealthy from doing.

3. A consequence of point 2 is that you shouldn't pigeonhole your skills into 1 discipline too early on or decide your education entirely on job market speculation.

4. Related to point 1 is that people often pursue "getting to the top" in an optimization-driven mindset but forget about the constraints. Optimization is often an exercise in figuring out which constraints you've forgotten to set. It's difficult to become the top in "machine learning and AI" because there's many people going after that. Same with "math". Or "writing literature". Or "genome engineering". But if you're pursuing gene editing, especially as applied to mammalian cells, using a synthetic RNA guide with an enzyme associated with the immunity systems of particular bacteria, perhaps you'll end up at the top of your game, receiving a Nobel, and getting funded for your unicorn startup.
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