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Senior Quantitative Researcher - Delta One
Posted Sep 11, 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 signal you built from scratch, from initial hypothesis to something that actually got traded. What was the idea, and where did it come from?
whether the candidate can originate independent hypotheses rather than just implementing others' ideas
Tell me about a time your backtest looked great but the strategy didn't hold up once it hit production or live markets. What went wrong and how did you find out?
self-awareness about overfitting and the gap between simulation and reality
Tell me about a disagreement you had with a trader over whether a strategy was ready to go live. How did you resolve it?
collaboration style and how they handle pushback from non-researchers with skin in the game
How do you decide when a research idea has been explored enough to abandon, versus when it just needs more iteration?
judgment and efficient use of research time, not just persistence
Tell me about a time you mentored or worked closely with a junior researcher on their project. What did you focus on improving?
whether they can raise the rigor of others' work, given the mentoring expectation in this role
Technical
Suppose you have a signal that predicts short-term price moves in a liquid futures contract. Walk me through how you'd design the backtest, including what you'd worry about getting wrong.
rigor in backtest design, awareness of look-ahead bias, transaction costs, and data snooping
How would you go about separating a genuine microstructure edge from something that's really just latency or queue position that you can't actually capture in practice?
depth of market microstructure knowledge and honesty about what's tradeable versus theoretical
Describe the delta hedging or execution research you've done. What was the tradeoff you were optimizing and how did you measure success?
practical grasp of hedging costs and execution quality, not just signal generation
Walk me through how you'd structure a simulation environment for testing signals across multiple futures products with different liquidity profiles and tick sizes.
hands-on experience with infrastructure and data pipeline design, not just modeling in isolation
What large market datasets have you worked with, and what's the messiest data problem you've had to solve before you could trust your results?
real hands-on experience with data quality issues at scale versus theoretical knowledge
Situational
Say you've got a medium-frequency cross-product alpha and a high-frequency microstructure signal on the same underlying. How do you think about combining them without one cannibalizing the other's risk budget or fills?
ability to reason about portfolio construction and interaction effects across time horizons
A signal that's been working well for six months suddenly degrades. Walk me through how you'd diagnose whether that's noise, regime change, or something structurally wrong with the model.
systematic approach to monitoring and attribution rather than jumping to conclusions
If a strategy's live PnL doesn't match what your attribution model predicted, what's your process for figuring out where the discrepancy is coming from?
discipline in strategy attribution and comfort reconciling model output with real trading results
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As published by Chicago Trading Company.
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