Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.
We’re building a new quantitative research team focused on pricing, market-making, and risk models for prediction markets. This is a highly hands-on role for someone who can operate end-to-end: data engineering, research, modeling, and close collaboration with traders, across sports and non-sports event markets and a range of contract types, including single-outcome markets, player props, and parlays.
Responsibilities:
- Build data foundation, transform raw data into pricing inputs
- Research and develop quantitative pricing, market-making, and risk models across sports, non-sports, player props, parlays, and correlated markets
- Model cross-market dependencies, correlations, and portfolio effects, especially for combinatorial products such as parlays
- Partner closely with traders to improve pricing logic, market coverage, and trading performance
- Build frameworks for backtesting, simulation, and model validation
- Create tools to monitor model performance, calibration, P&L attribution, and live trading outcomes
- Help define the tooling, workflow, and research standards for a new team
Requirements:
- Strong quantitative background in statistics, math, ML, economics, or a related field
- Experience building models in trading, sports, betting, prediction markets, or similar domains
- Strong Python/data skills and comfort owning data pipelines as well as modeling
- Ability to move quickly from raw data to research insight to production-ready mode
- High ownership, strong communication skills and comfortable with fast-paced high growth environment
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