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About the Role
We are hiring a Prediction Markets Quant Engineer to build research and trading infrastructure for operating in prediction markets. You will design models that estimate event probabilities, detect mispricing, size positions, and manage risk, then translate them into reliable end-to-end systems. This role sits at the intersection of quant research, engineering, and market microstructure.
Responsibilities
- Develop probabilistic models to forecast outcomes of real-world events.
- Combine heterogeneous signals into calibrated probability estimates.
- Build pricing and edge frameworks.
- Design evaluation methods for predictive models.
- Identify and exploit mis-pricings across contracts/venues.
- Design cross-market arbitrage and relative-value strategies.
- Build position sizing and risk frameworks.
- Enforce probability coherence and portfolio optimization for multi-outcome markets.
- Build data pipelines and real-time services for ingesting, cleaning, and versioning market and external data.
- Implement execution tooling, including order management and monitoring.
- Create dashboards and alerts for performance, exposure, and model health.
- Ensure reproducibility through experiment tracking, model registry, CI/CD, and robust testing.
- Work closely with trading, risk, and compliance stakeholders.
- Document models, assumptions, failure modes, and operating procedures.
- Participate in incident reviews and continuous improvement.
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.
- Some schedule flexibility may be required around major event windows.
- Self-motivated, detail-oriented, and comfortable working in a dynamic, startup-like environment.
- Possess the pre-existing right to work in Zurich, London, New York, or Hong Kong without company sponsorship.
Skills
- Python
- Statistics
- Probability
- Machine Learning
- Backtesting
- Model Evaluation
- Log Loss
- Brier Score
- Calibration
- Expected Value
- Position Sizing
- Risk Budgeting
- Correlation
- Liquidity Constraints
- SQL
- Pandas
- NumPy
- SciPy
- PyTorch
- Scikit-learn
- Airflow
- dbt
- Kafka
- Postgres
- BigQuery
- Docker
- Kubernetes
- CI/CD
- GitHub Actions
- Prometheus
- Grafana
- OpenTelemetry
- Forecasting
- Sports Analytics
- Political Modeling
- Event-Driven Trading
- Market Making
- Liquidity Modeling
- NLP
- Prediction Market Mechanics
- Bayesian Methods
- Probabilistic Programming
- Stan
- PyMC
- Ensemble Methods
- Online Evaluation
- Offline Evaluation
- Data Leakage Prevention
- Model Governance
Location
- Zurich
- London
- New York
- Hong Kong
Work Type
- Onsite
Education Level
- Degree in Quantitative Finance, Mathematics, Computer Science, Statistics, or a related quantitative field.
About the Company
- G-20 Group is a leading cross-asset trading firm active in delta-one and derivatives markets.
- Established in 2010, G-20 offers liquidity solutions, treasury management, and institutional advisory services.
- We are supported by an outstanding team of professionals, with a robust global presence in EMEA, Americas, and APAC.
- Join G-20 and be a part of a team that is at the forefront of financial markets, driving innovation and excellence in the sector.