Quantitative Trader/Researcher – Systematic Options Trading
Posted Oct 2, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Walk me through a systematic options strategy you designed. What was the edge, and how did you know it was real and not noise?
What they are checking: Whether they can separate a genuine edge from a backtest artifact, which is the core skill in systematic trading.
Tell me about a strategy or model you believed in that stopped working. How did you notice, and what did you do?
What they are checking: Honesty about decay and failure, plus the speed and method they use to detect it.
What would you do
An automated quoting system keeps getting picked off right after large moves in the underlying. How would you figure out what's going wrong?
What they are checking: Whether they think about adverse selection and latency together instead of blaming the pricing model by default.
Your systems run alongside operational options traders. How would you handle a trader who wants to override the model during a volatile session?
What they are checking: Whether they can respect trader judgment while protecting the integrity of a systematic process.
If you joined and saw an obvious improvement to a system someone else built, how would you go about proposing it?
What they are checking: Collaboration style on a team where shared systems and ego can collide.
About the work
How do you decide where to place an order relative to theoretical value when you're making markets in options?
What they are checking: Whether they understand the tradeoff between fill probability and edge per fill, which drives execution performance.
Explain how delta, gamma and vega exposure change for a short straddle as expiration approaches.
What they are checking: Fluency with option Greeks at the level needed to reason about risk in real time without a reference sheet.
You train a model that predicts short-term price moves and it looks great in-sample. What do you do before you let it trade?
What they are checking: Discipline around overfitting, lookahead bias and out-of-sample testing before real money is at risk.
What machine learning methods have you found actually useful on noisy financial data, and which ones disappointed you?
What they are checking: Practical judgment about model choice when signal-to-noise is low, not just familiarity with buzzwords.
How would you measure whether a change to your execution model actually improved trading performance?
What they are checking: Whether they can design a clean comparison in a live market where conditions never repeat exactly.
You flip a fair coin until you see two heads in a row. What's the expected number of flips?
What they are checking: Quick, clean probabilistic reasoning under pressure, a staple of quantitative trading interviews.
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As published by Optiver.