JobsJump Trading

Campus Quantitative Researcher, PhD (Intern)

Jump Trading · Chicago, IL · Front Office

Posted Aug 18, 2026 · We last checked this listing on Sep 20, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

Behavioral

Walk me through a research project from your PhD, start to finish. What was the hypothesis, how did you test it, and what did you actually conclude?

whether the candidate can structure and narrate rigorous research end to end, not just describe results

Tell me about a time your initial hypothesis was wrong. How did you figure that out, and what did you do next?

intellectual honesty and whether failure leads to understanding rather than abandonment

Explain a technical concept from your research area to me as if I have no background in it.

communication skill and ability to translate complex work for non-specialist mentors and traders

Tell me about a time you had two competing approaches to a problem and had to choose one. What made you pick it over the alternative?

ability to articulate tradeoffs and defend decisions with reasons, not just intuition

Describe a stretch of your PhD where things weren't working for a long time. What kept you going, and how did you know when to change direction versus keep pushing?

perseverance and judgment about when persistence becomes stubbornness

Tell me about a time you had to present a negative or null result to people who were hoping for a positive one. How did you handle that conversation?

ability to communicate honest, sometimes disappointing findings clearly and without spin

Technical

Suppose you're handed a large, messy dataset with a lot of missing and noisy values and told to find signal in it. How do you start?

practical data-cleaning instincts and whether they jump straight to modeling without understanding the data

How do you decide when a result is statistically meaningful versus just noise, especially with financial or time-series data?

grasp of statistical rigor and awareness of pitfalls like multiple testing and non-stationarity in market data

You inherit someone else's Python code with a bug you can't immediately explain. Walk me through how you'd debug it.

comfort reading and reasoning through unfamiliar code, a stated requirement of the role

How would you go about engineering features from a raw dataset you know nothing about, say tick-level trade data, to find something predictive?

concrete feature engineering instincts applied to an unfamiliar, market-flavored dataset

Situational

You've built a model that backtests beautifully but you're skeptical of it. What would make you distrust your own good result?

skepticism and understanding of overfitting, look-ahead bias, and data leakage

If your mentor gives you a project direction but you think a different angle would be more promising, what do you do?

initiative balanced with judgment about bringing the team along rather than going rogue

If your model performs well in testing but you only get one shot to validate it against live markets, how do you think about that risk?

understanding that live market validation is the real test and how they'd manage uncertainty under that constraint

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The full job description

As published by Jump Trading.

<p>Jump Trading Group is committed to world class research. We empower 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. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incenting 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.</p> <p>Our trading teams are each comprised of a dynamic group of traders, quantitative researchers, and engineers who work together to examine the global markets, seeking to understand the complexities of various traded products and exchanges. They leverage their impeccable statistical analysis and data mining skills, using the results of their research to make forecasts and develop profitable predictive trading models.</p> <h2><strong>About the Role</strong></h2> <p>The PhD quant research internship is an intensive 10-week program designed to show you what it's like to do research at Jump: real problems, real data, real markets. The program runs in person during Summer 2027 in our Chicago and New York offices. The first two weeks are focused training covering our research process, machine learning, statistics, trading and market mechanics, Python, and the infrastructure you'll use all summer. From there, you'll be matched with a trading team based on your background and interests, and spend the remaining weeks working 1:1 with experienced researchers on a real-world project tied to live business needs. You'll learn the craft working alongside people who have spent years practicing it.</p> <p>Research at Jump spans every asset class and a full range of time horizons, from high frequency to strategies that hold for days and weeks. Teams work across the spectrum of methods, from hand-crafted signals and rigorous classical statistics to deep learning models in production. Your project will reflect your team's needs, but the craft is the same everywhere: form well-educated hypotheses, construct rigorous tests, interpret results in a statistically sound way, and when an idea fails, understand why before moving on. One excellent, fully understood result is worth more here than a dozen shallow ideas. And every result is tested where it counts: against the live market itself.</p> <p>The program is open to currently enrolled PhD students. The internship is one of the main pathways to a full-time offer at Jump Trading.&nbsp;</p> <h2><strong>What You'll Do</strong></h2> <ul> <li>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</li> <li>Collect, clean, and explore large datasets (some clean, some noisy, some very noisy) and engineer features that turn raw data into predictive signal</li> <li>Build, fit, and evaluate models on our supercomputing grid, and present your results to your team throughout the summer, culminating in a final presentation</li> <li>Receive daily 1:1 mentorship from experienced quant researchers, with growing autonomy and compute as the summer progresses</li> <li>Other duties as assigned or needed.</li> </ul> <h2><strong>Skills You'll Need</strong></h2> <ul> <li>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</li> <li>Systematic research thinking: the ability to form well-educated hypotheses, design rigorous tests, and draw statistically sound, generalizable conclusions. No matter your area, these are the fundamental aspects of a good researcher, and it is no different at Jump Trading.</li> <li>Ownership of your research: the ability to explain the choices you made, the alternatives you considered and rejected, and why your approach won. Every idea demands a premise, and every rejection deserves a reason</li> <li>Experience conducting an in-depth research project with real-world data</li> <li>Programming experience in Python, with the ability to read, understand, and debug code, including code you didn't write</li> <li>Communicative and collaborative working style, sharing results early and often and treating mentors' time as a resource to use, not conserve</li> <li>Creativity and initiative to explore ideas beyond those suggested to you, with the judgment to bring your team along as you do</li> <li>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</li> <li>Reliable and predictable availability required</li> </ul> <p><strong>Nice to have:</strong></p> <ul> <li>Proficiency in C++ and/or Python (either works, and both is better)</li> <li>Familiarity with financial markets. No prior knowledge of finance or trading is necessary; we will give you the training that you need.</li> </ul> <p>INTERNATIONAL STUDENTS are encouraged to apply. We accept students eligible for CPT/OPT and we sponsor work visas for full-time positions.</p> <p>The estimated base salary for this role is $300,000 per year.</p>

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