<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <strong>About G20 Group<br/><br/></strong>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.<br/><br/><strong>Role Overview<br/><br/></strong>We are hiring a <strong>Prediction Markets Quant Engineer</strong> 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).<br/><br/>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.<br/><br/><strong>Responsibilities<br/><br/></strong><em><strong>Modeling &amp; Research <br/><br/></strong></em><ul><li>Develop probabilistic models to forecast outcomes of real-world events (e.g., elections, macro releases, sports, policy decisions, industry milestones). </li><li>Combine heterogeneous signals (time series, text/news, market data, polling/alternative data, fundamentals, expert priors) into calibrated probability estimates. </li><li>Build pricing and edge frameworks: fair value, uncertainty bands, expected value, and model drift/regime diagnostics. </li><li>Design evaluation methods (proper scoring rules like log loss/Brier score, calibration curves, back-tests with realistic costs and constraints). <br/><br/></li></ul><em><strong>Trading &amp; Market Design (Applied) <br/><br/></strong></em><ul><li>Identify and exploit mis-pricings across contracts/venues; design cross-market arbitrage and relative-value strategies where feasible. </li><li>Build position sizing and risk frameworks (Kelly variants, drawdown/risk budgets, scenario stress tests, liquidity/impact-aware sizing). </li><li>For multi-outcome markets: enforce probability coherence (no-arb constraints, normalization) and portfolio optimization across correlated contracts. <br/><br/></li></ul><em><strong>Engineering &amp; Production <br/><br/></strong></em><ul><li>Build data pipelines and real-time services for ingesting, cleaning, and versioning market + external data. </li><li>Implement execution tooling: order management, smart routing (where applicable), monitoring, and automated safeguards. </li><li>Create dashboards/alerts for performance, exposure, model health (calibration, drift), and operational integrity. </li><li>Ensure reproducibility: experiment tracking, model registry, CI/CD, and robust testing. <br/><br/></li></ul><em><strong>Collaboration &amp; Governance <br/><br/></strong></em><ul><li>Work closely with trading/risk/compliance stakeholders to translate research into controlled deployment. </li><li>Document models, assumptions, failure modes, and operating procedures; participate in incident reviews and continuous improvement. <br/><br/></li></ul><strong>Requirements<br/><br/></strong><ul><li>Degree in Quantitative Finance, Mathematics, Computer Science, Statistics, or a related quantitative field. </li><li>Strong engineering skills with Python (required); experience with production systems and data engineering. </li><li>Solid foundation in statistics, probability, and machine learning (calibration, uncertainty, causal pitfalls, time-series). </li><li>Experience building backtests and evaluating predictive models with appropriate metrics (e.g., log loss/Brier, calibration). </li><li>Familiarity with trading concepts: expected value, position sizing, risk budgeting, correlation, liquidity constraints. </li><li>Ability to communicate clearly about model assumptions, limitations, and risk. </li><li>Some schedule flexibility may be required around major event windows </li><li>Self-motivated, detail-oriented, and comfortable working in a dynamic, startup-like environment. <br/><br/></li></ul><strong>Preferred / Desirable Experience<br/><br/></strong><ul><li>Prior work in forecasting, sports analytics, political modeling, event-driven trading, or market-making/liquidity modeling. </li><li>Experience with NLP for news/social/media signals; knowledge graphs or information retrieval for event resolution. </li><li>Knowledge of prediction market mechanics (order books vs AMMs, fee structures, market manipulation/anti-manipulation signals). </li><li>Proficiency with SQL; experience with streaming systems (Kafka), workflow orchestration (Airflow), and cloud (AWS/GCP/Azure). </li><li>Experience with Bayesian methods, probabilistic programming (Stan/PyMC), or ensemble methods. </li><li>Familiarity with rigorous experimentation: online/offline evaluation, data leakage prevention, and model governance. <br/><br/></li></ul><strong>Tech Stack <br/><br/></strong><ul><li>Python, SQL, pandas/numpy/scipy, PyTorch/sklearn </li><li>Airflow/dbt, Kafka (or equivalents), Postgres/BigQuery </li><li>Docker, Kubernetes (optional), CI/CD (GitHub Actions) </li><li>Observability: Prometheus/Grafana, OpenTelemetry (or equivalents) <br/><br/></li></ul><strong>Locations and Right to work</strong>: This role can be based out of our Zurich, London, New York or Hong Kong office. Only candidates who possess the pre-existing right to work in one of the locations above without company sponsorship need apply.<br/><br/>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. </div>