- Joined
- 9/29/23
- Messages
- 19
- Points
- 3
Hi there! I have been lucky enough to receive admits from two of my top programs: MIT MFin and MSML at CMU (have also applied to MSCF round 2, but that decision will come out in May). While MSML (offered by CMU SCS) is not an MFE program, I will really appreciate if the Quantnet community can help me make an informed choice.
The target is to get into top-tier buyside quantitative research (QR) roles (not the trading roles) - think extensive use of DS/ML and Math in feature engineering, alpha generation, portfolio construction/management etc. I am particularly targeting QR roles rather than trading since latter may too many fast and accurate decisions per day, which I do not consider my forte (other than it being really stressful). The target firms are the whole buyside spectrum from Jane Street/HRT/Two Sigma to Trexquant/BAM etc.
From what I have gathered:
Pros for MIT MFin:
- Academic flexibility: can be made to be focused exactly on skills needed in QR like Stats, ML etc. Plus, I don't really mind their finance core (strong understanding of finance fundamentals is needed anyway)
- Quant firms have traditionally been recruiting at MFE/MFin/MSCF programs
- Very strong university brand name
- Proseminars and action lab: ample opportunities to work on industry projects in quant finance
- More targeted peer group and better networking opportunities: everyone is really into finance obviously!
- Ability to switch to trading roles in case I change my mind later
- Solid backup option of working as a sellside quant if I don't get into a buyside firm (all the major banks recruit here)
- Harvard cross-registration: minor point but can also get the Harvard classroom experience
Cons for MIT MFin:
- Lack of top-tier funds/shops: Firms like Jane Street, Two Sigma, Five Rings don't really recruit MFEs (except Princeton MFins maybe).
- More trading roles than QR roles: Somehow MIT MFin students have traditionally been going more into trading roles when it comes to buyside firms (trading @ Citadel, IMC, Jump etc.) than QR. QR seems more common in sell-side. Perhaps QR roles prefer MIT PhDs instead?
- Not really an MFE degree: though I will do the Financial Engg concentration, it's still an MFin degree. Don't know how "mathematically rigorous" employers regard it.
- Expensive $120K in tuition + living in Cambridge
Pros for CMU MSML:
- CMU is #1 for AI/ML; MSML is top program for ML there (among many other masters programs there); <3-4% acceptance rate
- Out of a cohort of ~50, 8-9 go into quant (of which 7 are in QR). The firms last year were Two Sigma, Jump Trading, HRT, SIG (top-tier firms)
- ML seems to gaining prevalence practically everywhere but more so in quant finance. A lot of quant job postings have begun using titles like "Data Scientist", "ML Researcher", "NLP Researcher" etc
- The backup option is FAANG - Data Scientist/Applied Researcher roles
- Higher median base salary ($160K) than MIT ($125K) - though MIT also includes several people going into fundamental finance/consulting/IB etc. (which don't have high entry-level base)
- Far better opportunities for research at CMU LTI/ML Dept/SCS; it's primarily a PhD feeder program, after all
- Rigorous coursework (though too intensive imo); will be ready for most challenges in ML
Cons for CMU MSML:
- Lack of orientation for finance and peers more interested in tech/AI
- Lack of industry projects and opportunities to interact with quant finance practitioners
- CMU known for really heavy curriculum, assignments
- Not really cheap either (still $90K)
- Less opportunities in Pittsburgh than in Boston
My background: CS major @ T10 STEM school in India, <1 yr workex as quant strat at a T3 investment bank, some research experience in DS/ML
Would really appreciate any inputs and thoughts!
The target is to get into top-tier buyside quantitative research (QR) roles (not the trading roles) - think extensive use of DS/ML and Math in feature engineering, alpha generation, portfolio construction/management etc. I am particularly targeting QR roles rather than trading since latter may too many fast and accurate decisions per day, which I do not consider my forte (other than it being really stressful). The target firms are the whole buyside spectrum from Jane Street/HRT/Two Sigma to Trexquant/BAM etc.
From what I have gathered:
Pros for MIT MFin:
- Academic flexibility: can be made to be focused exactly on skills needed in QR like Stats, ML etc. Plus, I don't really mind their finance core (strong understanding of finance fundamentals is needed anyway)
- Quant firms have traditionally been recruiting at MFE/MFin/MSCF programs
- Very strong university brand name
- Proseminars and action lab: ample opportunities to work on industry projects in quant finance
- More targeted peer group and better networking opportunities: everyone is really into finance obviously!
- Ability to switch to trading roles in case I change my mind later
- Solid backup option of working as a sellside quant if I don't get into a buyside firm (all the major banks recruit here)
- Harvard cross-registration: minor point but can also get the Harvard classroom experience
Cons for MIT MFin:
- Lack of top-tier funds/shops: Firms like Jane Street, Two Sigma, Five Rings don't really recruit MFEs (except Princeton MFins maybe).
- More trading roles than QR roles: Somehow MIT MFin students have traditionally been going more into trading roles when it comes to buyside firms (trading @ Citadel, IMC, Jump etc.) than QR. QR seems more common in sell-side. Perhaps QR roles prefer MIT PhDs instead?
- Not really an MFE degree: though I will do the Financial Engg concentration, it's still an MFin degree. Don't know how "mathematically rigorous" employers regard it.
- Expensive $120K in tuition + living in Cambridge
Pros for CMU MSML:
- CMU is #1 for AI/ML; MSML is top program for ML there (among many other masters programs there); <3-4% acceptance rate
- Out of a cohort of ~50, 8-9 go into quant (of which 7 are in QR). The firms last year were Two Sigma, Jump Trading, HRT, SIG (top-tier firms)
- ML seems to gaining prevalence practically everywhere but more so in quant finance. A lot of quant job postings have begun using titles like "Data Scientist", "ML Researcher", "NLP Researcher" etc
- The backup option is FAANG - Data Scientist/Applied Researcher roles
- Higher median base salary ($160K) than MIT ($125K) - though MIT also includes several people going into fundamental finance/consulting/IB etc. (which don't have high entry-level base)
- Far better opportunities for research at CMU LTI/ML Dept/SCS; it's primarily a PhD feeder program, after all
- Rigorous coursework (though too intensive imo); will be ready for most challenges in ML
Cons for CMU MSML:
- Lack of orientation for finance and peers more interested in tech/AI
- Lack of industry projects and opportunities to interact with quant finance practitioners
- CMU known for really heavy curriculum, assignments
- Not really cheap either (still $90K)
- Less opportunities in Pittsburgh than in Boston
My background: CS major @ T10 STEM school in India, <1 yr workex as quant strat at a T3 investment bank, some research experience in DS/ML
Would really appreciate any inputs and thoughts!
