Quantitative Researcher - Prediction Markets
Posted Aug 14, 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
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?
What they are checking: 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?
What they are checking: 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?
What they are checking: 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?
What they are checking: intellectual honesty and whether mistakes actually change their process going forward
What would you do
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?
What they are checking: 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?
What they are checking: prioritization instinct and whether they understand that data quality often matters more than model sophistication
About the work
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?
What they are checking: 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?
What they are checking: 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?
What they are checking: 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?
What they are checking: 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.
What they are checking: 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.
What they are checking: whether they can balance model sophistication against the need for traders to trust and act on outputs quickly
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Practice this out loudThe full job description
As published by Akuna Capital.