Campus Quantitative Researcher, PhD (Intern)
About The Position
Jump Trading Group is committed to world-class research, empowering exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique, fostering constant innovation through fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns; we design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems. Our trading teams are comprised of dynamic groups of traders, quantitative researchers, and engineers who collaborate to examine global markets, understand complexities of various traded products and exchanges, and leverage impeccable statistical analysis and data mining skills to make forecasts and develop profitable predictive trading models. The PhD quant research internship is an intensive 10-week program designed to immerse participants in the research environment at Jump, tackling real problems with real data and markets. The program runs in person during Summer 2027 in our Chicago and New York offices. The initial two weeks focus on training in our research process, machine learning, statistics, trading and market mechanics, Python, and the infrastructure used throughout the internship. Following training, interns are matched with a trading team based on their background and interests, working one-on-one with experienced researchers on a real-world project tied to live business needs. Interns will learn the craft alongside seasoned professionals. Research at Jump spans all asset classes and time horizons, from high frequency to strategies lasting days or weeks, utilizing a spectrum of methods from hand-crafted signals and classical statistics to deep learning models. The core of the work involves forming well-educated hypotheses, constructing rigorous tests, interpreting results statistically, and understanding failures. The program is open to currently enrolled PhD students and serves as a primary pathway to a full-time offer at Jump Trading.
Requirements
- Currently pursuing a PhD in Statistics, Mathematics, Computer Science, Physics, or any highly quantitative field; recent researchers have come from fields as varied as Electrical Engineering, Operations Research, and Economics
- Systematic research thinking: the ability to form well-educated hypotheses, design rigorous tests, and draw statistically sound, generalizable conclusions.
- Ownership of your research: the ability to explain the choices you made, the alternatives you considered and rejected, and why your approach won.
- Experience conducting an in-depth research project with real-world data
- Programming experience in Python, with the ability to read, understand, and debug code, including code you didn't write
- Communicative and collaborative working style, sharing results early and often and treating mentors' time as a resource to use, not conserve
- Creativity and initiative to explore ideas beyond those suggested to you, with the judgment to bring your team along as you do
- Perseverance: successful research is the result of lots of failure and intellectual risk-taking, and a PhD is often proof that you can stay with a hard problem for years without quitting
Nice To Haves
- Proficiency in C++ (either works, and both is better)
- Familiarity with financial markets. No prior knowledge of finance or trading is necessary; we will give you the training that you need
Responsibilities
- Match with a trading team and own a research project end to end, in areas such as predictive modeling, alpha research on new datasets, and improving the models and systems behind live trading
- Collect, clean, and explore large datasets (some clean, some noisy, some very noisy) and engineer features that turn raw data into predictive signal
- Build, fit, and evaluate models on our supercomputing grid, and present your results to your team throughout the summer, culminating in a final presentation
- Receive daily 1:1 mentorship from experienced quant researchers, with growing autonomy and compute as the summer progresses
- Other duties as assigned or needed.
Benefits
- The estimated base salary for this role is $300,000 per year.
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What This Job Offers
Career Level
Intern
Education Level
Ph.D. or professional degree
