Need Help Deciding: Duke MSS vs NYU MFE for Fall 2025 (International Student)

[Valeria]

Background​

• Education: Shanghai Jiao Tong University + University of Michigan dual degree
• Majors: ECE + Data Science with Math minor
• Status: International student

My Goals​

• Not opposed to pursuing a PhD later
• After a long application season, I realize I'm not very competitive in the MFE space
• As an international student, not fixated on staying in the US
• Primary goal: Find a well-paying job
• Career target: Quant trading (preferably discretionary trader or investment bank SNT rather than QR)
• Realistic about my odds: My academic background is average for quant roles, so not expecting to land buy-side positions immediately

Current Dilemma​

• I'm torn between Duke's Statistics program vs. a top 10 MFE program
• While I'm planning to work as a quant, I'd prefer QT over QR (specifically discretionary quant trader or SNT-type roles)
• Concerned that MFE degrees may be losing some of their prestige/value
• My only MFE offer so far is from NYU Tandon
• Worried about NYU's program quality after the passing of the program director
• Starting to feel like program variance matters less than individual performance ("MFE program variance << individual variance")
• Wondering if I should just choose an affordable program with a lighter course load (Columbia seems too expensive)

Offers Received​

• Duke MSS
• NYU Tandon MFE

Still Waiting On​

• UNC Financial Mathematics
• Cornell MFE
• Columbia MFE
• Columbia Financial Mathematics
• CMU MSCF (submitted in second round)

Decision Process​

• If I get into CMU, I'll definitely go there
• But between my current options (and potentially others if I receive more offers), what would you recommend?
• Is choosing a more affordable program with a manageable curriculum the right approach?

Additional Notes​

• Didn't apply to Baruch or Georgia Tech because I understand I might need to return to China after graduation due to visa/job market challenges

Would appreciate insights on Duke's statistics program vs NYU MFE specifically for quant trading paths, and whether program reputation truly matters long-term for the type of roles I'm targeting. Thanks!

 

[Andy Nguyen]

Hi Valeria,
Thank you for posting and sharing your profile. It sounds like you've carefully considered your options and priorities, which is a great start.
It would be extremely helpful if you can add the application timelines of those great programs you applied to in the Tracker. This way, our members can follow and help you decide as you receive results from other programs.
In this uncertain market, it is imperative to pick the right programs that prepare you for the job market.

Here is an idea: can you take some C++ and Python courses before starting the program in the Fall, prepare for interviews (doing prep books, LeetCode, etc) and use the Duke's network or career support to break in?
Statistics is one of the main majors for people working in quant finance. This is choosing between the 2 current options. If you receive more admits, you have more options.

What do you think?

 

[Valeria]

Hi Valeria,
Thank you for posting and sharing your profile. It sounds like you've carefully considered your options and priorities, which is a great start.
It would be extremely helpful if you can add the application timelines of those great programs you applied to in the Tracker. This way, our members can follow and help you decide as you receive results from other programs.
In this uncertain market, it is imperative to pick the right programs that prepare you for the job market.

Here is an idea: can you take some C++ and Python courses before starting the program in the Fall, prepare for interviews (doing prep books, LeetCode, etc) and use the Duke's network or career support to break in?
Statistics is one of the main majors for people working in quant finance. This is choosing between the 2 current options. If you receive more admits, you have more options.

What do you think?

Hi Andy,
Thank you so much for your kind reply and thoughtful suggestions—I really appreciate you taking the time to share your insights. I will definitely update the Tracker soon with the application timelines for the programs I’ve applied to.

For background, my undergraduate degree is in Data Science with a double major in Electrical and Computer Engineering, along with a minor in Mathematics, so I’ve had solid exposure to coding in both Python and C++. I’m also planning to continue brushing up on both languages and work through LeetCode and interview prep materials before the fall.

I wanted to kindly ask your thoughts on one thing—between Duke MSS and NYU MFE, I’ve noticed that the Duke program is sometimes described as being more academically or PhD-oriented, with a stronger emphasis on Bayesian methods and theoretical statistics. I’m a bit concerned that it may not be as targeted toward quant finance roles as an MFE program like NYU’s. Could I ask what makes you lean toward Duke in this case?

Thank you again for your time and guidance!

 

[Andy Nguyen]

Duke Stats is more suitable if you want to do a PhD whereas most of the MFE programs are terminal degrees.
In MFE programs, you start in early summer with interview workshops, career prep, math and programming bootcamps, etc.
The courses are designed with industry needs in mind. This is how it works in principle.
The stats programs are purely academic and usually offered by the math depts. They have no career service component like the MFE programs.
That said, you can take initiatives to market yourself to quant jobs. If you look at listings for entry level quant jobs, the degrees requirement is CS, Stats, MFE, Math, etc so having a Stats degree would not exclude you.

I'm not familiar with Duke Stats program but I think it may be a valid option to find a quant job as well as other industry or continue on PhD.
One thing you should do is to check where alumni of Duke MSS end up working in finance. If many of them end up in roles you want to target, I think they can provide some insights on how to get there.