Prediction Markets Quantitative Engineer

About G20 Group G-20 Group is a cross-asset trading firm headquartered in Switzerland, trading delta-one and derivatives markets globally. We combine startup agility with institutional-grade experience in proprietary trading, technology, and quantitative finance. Role Overview We are hiring a Prediction Markets 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). This role sits at the intersection of quant research, engineering, and market microstructure, and is ideal for someone who enjoys shipping robust systems as much as developing models. Responsibilities 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. Build pricing and edge frameworks: fair value, uncertainty bands, expected value, and model drift/regime diagnostics. Design evaluation methods (proper scoring rules like log loss/Brier score, calibration curves, back-tests with realistic costs and constraints). 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). For multi-outcome markets: enforce probability coherence (no-arb constraints, normalization) and portfolio optimization across correlated contracts. 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. Create dashboards/alerts for performance, exposure, model health (calibration, drift), and operational integrity. Ensure reproducibility: experiment tracking, model registry, CI/CD, and robust testing. Collaboration & Governance Work closely with trading/risk/compliance stakeholders to translate research into controlled deployment. 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 (required); experience with production systems and data engineering. Solid foundation in statistics, probability, and machine learning…