Non-MFE/PhD entry into Quant?

[Abhijeet Avhale]

Hi everyone,

I’d really appreciate your thoughts on something I’ve been navigating recently.

I was admitted to Cornell’s MFE program this fall, but unfortunately, my visa was rejected. Since then, I’ve been rethinking my path into the quant finance space and would love to hear perspectives from people who’ve made unconventional transitions or have any ideas for it.

My background in short:

• Master’s in Physics (India), with a thesis in neural networks (Germany)
• Solid experience in data science research
• Exposure to stochastic analysis (e.g., Itô’s integral, Black–Scholes), but only through coursework and a few articles written during an internship
• Some familiarity with stochastic optimization for ML (SGD, Adam, etc.), but no previous hands-on optimization projects or coursework in finance

Looking at Cornell’s MFE curriculum, I see it split across stochastic processes, optimization, and data science. The first and third resonate with my background, but optimization is where I’m clearly missing practical exposure.

My dilemma:

• People often say that breaking into quant finance without a PhD or a traditional MFE is difficult. Is there any way?
• Given my profile, what’s the most realistic strategy to enter the quant space(Anywhere Globally - DE, UK, IN)?
• I could defer and try again next year, but there’s no guarantee the visa clears — so I don’t want to put all my eggs in that basket.

I would like to cover up some optimisation, but self-studied coursework does not go in resumes and is hard to convince from just a cover letter.
I’ve attached my resume (already public, with personal details redacted). Any insights, advice, or even examples of non-traditional paths into quant roles would mean a lot.

Thanks in advance!