Transition from PhD Astronomy to Quant

[into_the_sky]

Hello everyone,

I will defend my PhD thesis in Astronomy - Celestial Mechanics, to be more precise, in December this year, but I still have a Postdoc contract next year if nothing changes.
My interest for Astronomy / academia wane as I go further in this direction and by now I would like to transition to a data science / quantitative finance jobs. My initial intention is actually data science but as I'm looking deeper, I found that a job as quant researcher is much more suitable for me.

I have strong background in computational physics / statistical analysis / stochastic process. Beside the required analytical thinking / problem solving skills for a PhD, I'm quite confident with my coding, which is primarily in Python, as my research for the last 4 years involves a lot of programing / data handling / parallel computing. I have great interest in machine learning and data science generally. I have implemented several algorithm of several papers that I found interesting in ML / statistics and I think I have no problems doing it for an trading algorithm in an analytical finance article for example.
Therefore I don't think I will have any problems transitioning to quantitative finance, I could be wrong though.

Now, I desire make the transition but I find the road ahead quite fuzzy and uncertain as I have no experience outside of academia before.
Should I apply for a quant job right now? Or I should spend a couple of months before to brush up my knowledge in finance, trading, and preparing for the interview?
More generally, what is the best path to take to make a smooth transition? Not only for me for future interested Astronomy graduates.

I'm an international student in France, by the way.

 

[Quakko]

I believe that you should definitely take two months (or more) to make sure you're ready before you apply for interviews.

Revise basic linear algebra, basic probability, stats, brain teasers, and coding. Need to be confident in all of them.

For coding, look up for example the book "cracking the coding interview" (there are similar ones for brain teasers and stats).

Are you at a top ranked institution in France? You probably already know this, but your chances of getting interviews depend not only on how good you are but also on the prestige of your university.

 

[EngTurnedQuant]

Just a bit of anecdotal evidence here, but I'm a PhD in Mechanical Engineering from a top 15 institution and have had very little luck in even getting interviews with trading firms or investment banks.

I've talked with some recruiters and their feedback was that my background didn't fit well with what they're looking for. If I were you, I'd definitely tailor your resume to highlight your proficiency in Python and your knowledge of stochastic processes, modeling and fitting data. For quant jobs these days, a lot of firms want solid machine learning and data science backgrounds and not just the math proficiency and critical thinking.

I'm actually trying to do a MFE to try and bolster my chances of getting in the door for interviews. This could just be me, but I've heard that it's a very competitive space and a hard area to break into. I'm finding that to be very true.

 

[Daniel Duffy]

Online Courses

quantnet.com

My feeling is MECH is out on a limb these days,