Quantitative Researcher - SVT
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
You have four to five years of alpha research in equity or index options. Tell me about the signal you're proudest of and how it performed live.
What they are checking: Whether their claimed experience translates into a real, measurable result rather than research that never traded.
Tell me about a time a model you trusted was wrong in a way you didn't expect. What did you change in your process?
What they are checking: Intellectual honesty and whether failures change how they work going forward.
How do you work with engineers when your research needs something the current infrastructure can't do?
What they are checking: Whether they can collaborate across functions to get research into production on a small team.
What would you do
This team is in its early stages. What would you want to build first, and why?
What they are checking: Whether they can set priorities and think like a founding member, not just execute a given task list.
Imagine your strategy loses money for three straight weeks. How do you decide whether to keep running it?
What they are checking: Ability to tell normal drawdown from broken edge without panicking or stubbornly holding on.
About the work
How would you build a volatility signal for index options, starting from nothing but historical data?
What they are checking: Their end-to-end research process, from hypothesis to feature design to validation.
What's the difference between implied and realized volatility, and how would you trade a persistent gap between them?
What they are checking: Core volatility trading intuition, including the costs and risks that eat into a seemingly obvious spread.
How do you account for transaction costs and market impact when you evaluate an alpha signal?
What they are checking: Whether they know a signal is only worth what survives execution in a fully automated strategy.
You find a signal with a strong Sharpe ratio but very few independent observations. How much do you trust it?
What they are checking: Statistical maturity about sample size, multiple testing and how easily a small dataset fools you.
How would you think about the volatility surface when designing an automated options strategy across strikes and expiries?
What they are checking: Whether they can reason about relative value across the surface rather than one option in isolation.
What novel execution technique have you seen or tried that improved fill quality in options?
What they are checking: Depth on the execution side, which the posting lists alongside signals and models.
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As published by Optiver.