Prediction Market Quant Engineer

Our client is a pioneer in Quantitative Trading systems in cross-asset markets. Headquartered in Switzerland, they operate at the intersection of Quantitative Research, Software Engineering and Trading. The team combines a start-up mindset with extensive experience in proprietary Trading, Technology and Quantitative Finance.

Our client is seeking an experienced Prediction Market Quant Engineer to build research and trading infrastructure for operating in prediction markets (event contracts) across multiple venues. You will design models that estimate event probabilities, detect mispricing, size positions, and manage risk - then translate them into reliable systems that run end-to-end (data → forecasting → execution → monitoring).

Modeling & Research

• Develop probabilistic models to forecast outcomes of real-world events (e.g., elections, macro releases, sports, policy decisions, industry milestones).
• Combine heterogeneous signals (time series, text/news, market data, polling/alternative data, fundamentals, expert priors) into calibrated probability estimates.

Trading & Market Design (Applied)

• Identify and exploit mis-pricings across contracts/venues; design cross-market arbitrage and relative-value strategies where feasible.
• Build position sizing and risk frameworks (Kelly variants, drawdown/risk budgets, scenario stress tests, liquidity/impact-aware sizing).

Engineering & Production

• Build data pipelines and real-time services for ingesting, cleaning, and versioning market + external data.
• Implement execution tooling: order management, smart routing (where applicable), monitoring, and automated safeguards.

• Degree in Quantitative Finance, Mathematics, Computer Science, Statistics, or a related quantitative field.
• Strong engineering skills with Python (required); experience with production systems and data engineering.
• Solid foundation in statistics, probability, and machine learning (calibration, uncertainty, causal pitfalls, time-series).
• Experience building backtests and evaluating predictive models with appropriate metrics (e.g., log loss/Brier, calibration).
• 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

Tech Stack

• Python, SQL, pandas/numpy/scipy, PyTorch/sklearn
• Airflow/dbt, Kafka (or equivalents), Postgres/BigQuery
• Docker, Kubernetes (optional), CI/CD (GitHub Actions)
• Observability: Prometheus/Grafana, OpenTelemetry (or equivalents)

Preferred / Desirable Experience

• Prior work in forecasting, sports analytics, political modeling, event-driven trading, or market-making/liquidity modeling.
• Experience with NLP for news/social/media signals; knowledge graphs or information retrieval for event resolution.
• Knowledge of prediction market mechanics (order books vs AMMs, fee structures, market manipulation/anti-manipulation signals).
• Proficiency with SQL; experience with streaming systems (Kafka), workflow orchestration (Airflow), and cloud (AWS/GCP/Azure).