Quantitative Researcher - Prediction Markets, Quant Trading

About the role

This role focuses on building and refining quantitative models for prediction markets, encompassing various categories like sports, non-sports, and correlated markets. The work involves transforming raw data into usable inputs for pricing, developing sophisticated models for market-making, and assessing risk. A key aspect is understanding and modeling dependencies and correlations between different markets, especially for complex products.

The successful candidate will work closely with traders, integrating research insights directly into trading strategies. This partnership aims to improve pricing logic, expand market coverage, and ultimately boost trading performance. The role also includes creating frameworks for backtesting, simulation, and validating models to ensure their accuracy and reliability.

Beyond model development, the position requires building tools to monitor model performance, calibration, and profit and loss attribution in live trading environments. As part of a new team, this individual will contribute to establishing the team's tooling, workflow, and research standards, playing a foundational role in shaping its operational practices.

The annual salary for this role is between $100,000 and $150,000 USD.

Skills that matter here

• Python: This role requires strong Python skills for data manipulation, building data pipelines, and developing quantitative models.
• data engineering: The position involves transforming raw data into usable inputs for pricing and risk models, requiring expertise in data engineering.
• quantitative modeling: A core responsibility is to research and develop quantitative pricing, market-making, and risk models for prediction markets.
• statistics: A strong background in statistics is essential for developing robust quantitative models and understanding data distributions.
• machine learning: Machine learning skills will be applied to develop and refine models for prediction market analysis and forecasting.
• economics: Knowledge of economics will help in understanding market dynamics, pricing mechanisms, and cross-market dependencies.

Who this role suits

• A person with a strong quantitative background comfortable moving quickly from raw data to research insights.
• Someone who thrives in a fast-paced, high-growth environment and takes high ownership of their work.
• An individual who enjoys collaborating closely with traders to improve practical trading outcomes.
• A professional who can contribute to defining new team standards and workflows.

From the employer

• 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

• 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