Quantitative Researcher Says to Be Brutally Honest with Yourself

Name: Sanha Cheong (he/him)

PhD: Physics (Experimental Particle Physics), Stanford University, 2025

What is your current job?

I am a Quantitative Researcher at Citadel Securities in Miami, Florida.

I develop valuation models, predictive models, etc. for financial products, develop trading strategies from such models, back-test, analyze, and improve trading strategies. At the day-to-day level, this involves a lot of applied math and statistics, and the ability to translate conceptual math into concrete codes is a key skill.

What is your favorite thing about your job?

I enjoy the application of various cutting edge quantitative skills, a fast-paced environment, and working with other like-minded people (very data-driven, competitive, etc.).

What is the most important skill you developed or experience you had during your PhD that now helps you in your current position?

On the technical side, software development, statistics, large-scale data analysis experience, and physics intuition.

On the non-technical side, communication and management skills, especially to people who are not exactly from the same background as mine, were developed through international, multi-disciplinary collaborations and teaching experiences.

How did you build the skills necessary for your current role?

Honestly, in my case, the PhD experience was quite a spot-on training for my role. The technical skills required are extremely similar, and my PhD experience in a large international collaboration transferred very well to working in a private-sector company of scale.

How did you find this position? What were the career steps you took to get to where you are now?

Company-hosted events on campus, which led to an internship, which then led to a full-time offer

PhD ➡️ summer internship ➡️ full-time return offer

If someone is interested in a similar role, what would you recommend they start doing now to prepare?

Math and statistics skills, the ability to translate the math in your mind into codes (Python and/or C++), and the experience of performing (medium- to large-scale) data analysis

Why did you decide to not pursue a career in academia?

Probably too many reasons to write down here.

Some of the biggest factors were the long post-doc time typical in my field (which means a long period of financial and geographical instability), the "isolation/loneliness" of working on something so remote from human life (I wanted to work on something closer to everyday human life), and the bureaucracy/politics involved in academic career.

I am not sure whether "difficult" is the right word, but it took me a long time to feel comfortable about this decision.

What advice do you have for someone getting their PhD and looking to pursue a career outside of academia?

Start looking early, stay open-minded, talk to a wide range of people who have gone through similar experiences, and think deeply and very honestly about what makes you happy.

It is very easy to fool oneself into thinking that you are happy with what you are doing now, even when that is not true. Be brutally honest with yourself.

Are there any components of your identity you would like to share, including how they have impacted your journey?

Being a first immigrant from a "middle-class" family in Asia and getting married somewhat early was probably a non-trivial factor. I definitely had finance in my mind when deciding to not pursue a post-doc.

Nobody in my family has pursued a degree in STEM or any graduate degree, so I had to figure out a lot of things on my own as I go. This wasn't easy, but it probably helped me make an unbiased, honest decision that focused on my own happiness.

And for those interested, what was your main area of research?

I worked at the ATLAS Experiment at the Large Hadron Collider (LHC), located at CERN. At the LHC, we accelerate protons to extremely high energies (99.9999991% of speed of light) and collide them, resulting in high-energy phenomena that are inaccessible in our everyday lives, but played an important role in the early stage of our universe.

My PhD focused on studying the properties of the Higgs boson, machine learning applications for data reconstruction/analysis at the LHC, and some hardware development (QA/QC, testing, etc.) for such experiments.