Quantitative Researcher - Prediction Markets
Posted Aug 14, 2026 · We last checked this listing on Sep 20, 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.
Behavioral
Tell me about a time you had to take a model from research into a live production system. What broke, and how did you find out?
whether they've actually shipped models to production and own the full lifecycle rather than just building notebooks
Describe a project where you worked closely with a trading desk or another non-research team to get something deployed. What was the friction, and how did you resolve it?
whether they can translate between research and trading language and handle the compromises that come with production deadlines
What's a sport or event type you think is currently mispriced or underserved by existing models, and why?
genuine passion for sports analytics and whether they think about markets, not just models, as their real interest
Tell me about a time your model or analysis was wrong in a way that mattered. How did you find out, and what did you change afterward?
intellectual honesty and whether mistakes actually change their process going forward
Technical
Walk me through how you'd build a live win-probability model for an in-play sports contract, say a basketball game, from scratch. What data would you need and how would you update it as the game progresses?
whether the candidate can structure a real-time probabilistic model and thinks about data latency and update frequency, not just static statistics
Say you're pricing player prop contracts and your historical data has a lot of missing or inconsistent stats across different data providers. How do you handle that in your pipeline?
practical data engineering skill and awareness that messy real-world sports data is the norm, not the exception
How would you go about validating that your in-play pricing model is actually well-calibrated, not just accurate on average?
understanding of calibration versus accuracy, a distinction that matters a lot when the model sets prices people trade against
If you had to price the probability a specific player scores over a certain number of points in the next five minutes of a game, what factors would you want in the model and which would you prioritize first?
ability to reason from scratch about a novel, sport-specific pricing problem under time pressure
Explain how you'd use a Poisson or similar count-based model to price a soccer match outcome, and where you think that approach breaks down in-play.
depth of statistical knowledge applied specifically to sports modeling, and awareness of model limitations
Walk me through a machine learning or optimization technique you've used where the interpretability of the model mattered as much as its raw performance.
whether they can balance model sophistication against the need for traders to trust and act on outputs quickly
Situational
Suppose your model says a team has a 70 percent chance to win, but the market is quoting 55 percent, and this persists for several minutes. What do you do?
judgment about trusting your own model versus deferring to the market, and whether they'd investigate before acting
If you had limited engineering resources and had to choose between building a better data capture pipeline or improving the model's calibration, which would you pick first and why?
prioritization instinct and whether they understand that data quality often matters more than model sophistication
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As published by Akuna Capital.
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