Xuci Sabrina Mei

Biography

Education

• M.S. in Computational Finance - Carnegie Mellon University (2024)
• B.S. in Science, Statistics & Machine Learning - Carnegie Mellon University (2023)

What inspired your interest in the quant/data science space, and how did you end up in your current role?

I’ve always enjoyed building things that connect models to real trading impact. My undergrad in Statistics and Machine Learning gave me a strong technical foundation in both theory and programming. During my internships, I saw how data-driven pricing and automation could reshape product workflows. That combination of coding, analytics, and market intuition naturally drew me to quant strategy. I’ve also been trading equities on my own for years. That hands-on experience has helped me better understand how model predictions interact with live market dynamics in a front desk environment.

How did the MSCF program prepare you for the technical and analytical demands of your role?

MSCF trained me to think both rigorously and pragmatically. There’s often a gap between technical thinking and what sales and traders actually need. Trading desks focus on speed, usability, and clarity—not on model complexity. In the program, technical work always came with practical constraints: Will this run fast enough? Is the logic intuitive? Can it handle edge cases? That mindset is directly applicable when working with front office users.

Looking back, which MSCF courses or professors had a lasting impact on your career?

The Financial Data Science sequence was especially useful — a lot of hands-on projects that felt like real desk work: data wrangling, building models under time pressure, and delivering something people could actually use. Simulation Methods for Option Pricing helped connect theory with how traders actually think about hedging and pricing complex products. And even though I didn’t love every math-heavy class, Stochastic Calculus gave me the foundation for working with financial models. A lot of the intuition carries over when thinking about hedging, Greeks, or modeling payoffs.

How do you stay current with evolving technologies and research in the quant space?

Being on the sell side gives great access to top-tier resources — internal research teams, notes from trading desk etc. I regularly read sell-side strategy notes. Outside of work, I follow a mix of academic papers and industry blogs.

What advice would you give to students who want to pursue a hybrid quant/data science career in finance?

Start with solid fundamentals — stats, probability, linear algebra, and clean coding habits. It’s tempting to jump straight into models, but real impact comes from knowing how things work under the hood.

Then, be prepared to invest time understanding systems. A lot of firms have internal data storage platforms, codebases, and pricing infrastructure that take time to navigate. It’s not glamorous, but learning how things plug together — and being able to quietly work through that complexity — makes a huge difference over time.

Also, think about the user. Whether it’s a trader, a PM, or another quant, the best tools are the ones that are fast, reliable, and intuitive to use.
 

What’s something you've learned on the job that challenged your expectations or assumptions from grad school?

One important lesson — and mistake I made early on — was assuming that I could solve every problem on my own.

In school, when you hit a roadblock, you dig into textbooks or ask the professor, but ultimately you’re expected to fix it yourself. In the workplace, though, some problems simply can’t be solved in isolation — they might involve systems, data ownership, or cross-team dependencies. Escalating early isn’t a sign of weakness, but of responsibility. The faster you raise an issue, the faster the team can align and move forward.