Looking for critique on my profile and university list

Hi friends,

I am in my penultimate year at a top Canadian university studying pure mathematics and computer science. I will be applying to MFE programs in the next year, and want to get a head start and pinpoint weaknesses in my application.

Major: Pure math and computer science

GPA: 3.75 cumulative, 3.9 math gpa, 3.8 cs gpa

GRE: not yet taken

Work Experience: Interned first year summer at a small quant prop. Talking to a few market making firms for a quant research internship in the upcoming summer. Started a quant account two years trading arbitrage in crypto and volatility in US/Euro equities, now at a respectable AUM.

Status: Canadian citizen

Relevant Coursework: functional/real analysis, PDE's, mathematical probability, numerical analysis next semester. Besides this, all my other courses for this year and the next are graduate math and cs courses that are not very applied, for example algebraic geometry and K-theory.

Research: Working on a research paper with a math Ph.D. student on various ML models applied to order book data.

Programming: Fluent in C, Python, Java, and not really a language but SQL. Aiming to learn C++ and release some projects before application season. I have a few open source projects written in python, for example a back-tester that supports complex derivatives pricing. Good knowledge of databases.

Programs:
- Baruch
- Columbia
- NYU
- Princeton mfin
- MIT mfin
- Berkeley
- CMU

I want an application that is airtight, as I have been working on this path ever since high school to get into quant finance. Any and all advice would be appreciated on how to add more, for example competitions I could do, etc.

Thanks a lot!
 
that's a pretty solid profile. programs like berkeley prefer people with some work experience, so 1-2 years of relevant work ex would really make it "airtight" but i think you should get into most programs anyway

like @IntoDarkness said try to ace the gre and focus on writing good essays and you should be good
 
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