Prediction Markets Quantitative Engineer
Skills
About the Role
You will build research and trading infrastructure for operating in prediction markets across multiple venues. You will develop probabilistic models to forecast real-world events, combine heterogeneous signals into calibrated probability estimates, and build pricing and edge frameworks. You will identify and exploit mispricings, design cross-market arbitrage and relative-value strategies, and build position sizing and risk frameworks. You will build data pipelines and real-time services, implement execution tooling, create dashboards and alerts, and ensure reproducibility through experiment tracking, model registries, CI/CD, and robust testing. You will work closely with trading, risk, and compliance stakeholders to translate research into controlled deployment, and you will document models, assumptions, failure modes, and operating procedures.
Requirements
- Degree in Quantitative Finance, Mathematics, Computer Science, Statistics, or a related quantitative field
- Strong engineering skills with Python
- Experience with production systems and data engineering
- Solid foundation in statistics, probability, and machine learning
- Experience building backtests and evaluating predictive models with appropriate metrics
- Familiarity with trading concepts: expected value, position sizing, risk budgeting, correlation, liquidity constraints
- Ability to communicate clearly about model assumptions, limitations, and risk
- Schedule flexibility around major event windows
- Self-motivated, detail-oriented, and comfortable in a dynamic, startup-like environment
Responsibilities
- Develop probabilistic models to forecast outcomes of real-world events
- Combine heterogeneous signals into calibrated probability estimates
- Build pricing and edge frameworks including fair value, uncertainty bands, and model drift diagnostics
- Design evaluation methods using proper scoring rules, calibration curves, and realistic back-tests
- Identify and exploit mis-pricings across contracts and venues
- Design cross-market arbitrage and relative-value strategies
- Build position sizing and risk frameworks
- Enforce probability coherence and portfolio optimization across correlated contracts
- Build data pipelines and real-time services for market and external data
- Implement execution tooling including order management, monitoring, and automated safeguards
- Create dashboards and alerts for performance, exposure, model health, and operational integrity
- Ensure reproducibility through experiment tracking, model registry, CI/CD, and robust testing
- Collaborate with trading, risk, and compliance stakeholders to translate research into controlled deployment
- Document models, assumptions, failure modes, and operating procedures