Quantitative AI/ML Engineer – Trading & Risk

Role purpose

Research and model development

• Translate approved hypotheses into features, labels, and models for behavioral classification, anomaly detection, exposure forecasting, and execution-quality analysis.
• Use trading activity, price movements, order timing, holding periods, execution data, and account history to develop models with clearly defined predictions.
• Investigate concentration, correlated activity, trading-pattern changes, and adverse-selection indicators using suitable statistical and machine-learning methods.
• Benchmark machine learning against transparent statistical and rules-based baselines and test whether complexity adds meaningful value.

Validation and interpretation

• Build chronological and walk-forward validation that reflects live information availability and prevents leakage from related accounts, overlapping labels, and future observations.
• Evaluate robustness across instruments, client cohorts, and market regimes, accounting for class imbalance, multiple testing, and small samples.
• Use realistic spreads, slippage, latency, liquidity constraints, and transaction costs in execution or hedging evaluations.
• Produce explainability, calibration, sensitivity, and stability analyses and document assumptions, limitations, and conditions for non-use.

Production and monitoring

• Package models for batch or online inference with versioned code, features, datasets, and dependencies; work with MLOps on deployment and rollback.
• Monitor data drift, prediction changes, performance deterioration, and operational failures, distinguishing market changes from broken data.
• Run shadow or controlled challenger assessments, with approval and independent challenge for material trading or risk applications.
• Investigate failures, refresh models through reviewed change procedures, and support communication of results to Dealing and Risk.

Essential qualifications

• 4+ years in quantitative research, applied machine learning, or financial data science, including production model implementation.
• Strong Python, SQL, probability, statistics, optimization, and time-series analysis skills.
• Hands-on experience with scikit-learn, XGBoost, LightGBM, or PyTorch and ability to select appropriate methods.
• Working knowledge of leveraged trading, FX/CFDs, or comparable electronic markets, including exposure, leverage, margin, and execution costs.
• Experience preventing data leakage, conducting out-of-sample validation, and assessing model stability in changing markets.
• Strong software engineering, experiment tracking, and technical documentation skills; communicate uncertainty without overstating predictions.
• Degree in Mathematics, Statistics, Computer Science, Physics, Engineering, or a related quantitative discipline; postgraduate study is advantageous.

Preferred experience

• Retail FX/CFD flow, broker risk books, hedging analytics, or execution-quality modeling.
• Real-time inference, high-frequency data, graph methods, or interpretable client segmentation.
• Deployment through monitored shadow testing and model review processes.