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
