Is it realistic to get into a top-10 MFE/QF program with 2.8 GPA?

[tlorenzi]

I have 2.8 GPA from SJSU with a degree in Applied Computational Math. I have 5 CS courses taken and micro/macro economics. My gpa is not a good indicator of my academic ability due to me not taking my undergrad seriously. I plan to go into the actuarial industry and pass all the exams required to get my designation. Exams include topics like probability theory, financial mathematics, statistical learning, etc. While I am pursuing the actuarial path, I am considering going for a CFA since the actuary experience should qualify for the necessary experience for a CFA. My thinking is the following: actuarial work experience, my actuarial exams, strong programming experience, and possibly a CFA would help overcome by bad GPA. I plan to spend 4-5 years in the actuarial industry before getting an MFE, and acing the quant section of the GRE.

I would also consider taking some courses like PDE, numerical analysis, or machine learning to boost my application's quality.

Will i be a strong candidate for colleges like Berkeley, NYU, or Baruch?

 

[IntoDarkness]

i would say find ur first job and work for several months then we can evaluate how to go forward

 

[binomial-torrent]

i would say find ur first job and work for several months then we can evaluate how to go forward

so i have a 3.1 overall gpa (3.5 math major gpa) from a semi-target undergrad (think NYU, umichigan, columbia, etc) and already got the job, working as a trading analyst in a buy-side asset management firm. don’t really use a lot of stats and linear algebra yet, just coding and basic math. is it the same idea (work for 1-2 years then evaluate) if i want to be a hedge fund/prop shop quant researcher?

 

[IntoDarkness]

at the current bouncing rate of young ppl, u can evaluate every few months. noticed many ppl with good ideas usually work at a company for a few months then going solo with their own thing

 

[binomial-torrent]

at the current bouncing rate of young ppl, u can evaluate every few months. noticed many ppl with good ideas usually work at a company for a few months then going solo with their own thing

have to pay back signing bonus and figure out what to do with my lease tho

will see if they’ll eventually let me get to the core research instead of just coding up stuff. they’re starting a NLP trading signal thing next year supposedly

 

[AppliedMath]

I have 2.8 GPA from SJSU with a degree in Applied Computational Math. I have 5 CS courses taken and micro/macro economics. My gpa is not a good indicator of my academic ability due to me not taking my undergrad seriously. I plan to go into the actuarial industry and pass all the exams required to get my designation. Exams include topics like probability theory, financial mathematics, statistical learning, etc. While I am pursuing the actuarial path, I am considering going for a CFA since the actuary experience should qualify for the necessary experience for a CFA. My thinking is the following: actuarial work experience, my actuarial exams, strong programming experience, and possibly a CFA would help overcome by bad GPA. I plan to spend 4-5 years in the actuarial industry before getting an MFE, and acing the quant section of the GRE.

I would also consider taking some courses like PDE, numerical analysis, or machine learning to boost my application's quality.

Will i be a strong candidate for colleges like Berkeley, NYU, or Baruch?

Huh? You're overthinking this. Why would you spend 4-5 years getting ready for get into a top MFE, just to spend 2 more years in the MFE - and then try to get quant job? Waste of time. You should get into the finance and quant finance space in now, have real experience, never waste time on an MFE and grow yourself otherwise. If you really want to burn 4-5 years, get a M.S. in Physics and start a Physics Ph.D. You'll become a quant for sure.

Your GPA is meaningless once you start working. I'd suggesting getting into Risk Analysis in Finance/Investments, doing the FRM or PRM, and moving from Risk to Quant Risk. It's more direct. Your plan is super roundabout, and lofty enough that you may as well be directly lofty.