Company Description Trade W is a leading multi-asset trading platform with over seven years of industry experience, providing global users with secure, convenient, and efficient access to the financial markets. We offer CFD trading across a wide range of asset classes — including forex, cryptocurrencies, stocks, indices, metals, and commodities — through our intuitive app and web platform.Launched in 2018 as the flagship brand of Tradewill Global LLC, Trade W was built on a customer-first philosophy and a vision to make trading success more accessible. Today, we continue to grow as a trusted platform, committed to empowering traders worldwide with equal opportunities for success.About the Role We are seeking a highly capable Risk Data Scientist to join our Dubai team and drive the development of large-scale risk analytics and real-time monitoring systems. You will work across high-frequency, derivatives, and multi-asset trading data to detect anomalies, optimize risk parameters, and support trading, routing, and market-making strategies. This role works closely with global risk, trading, and engineering teams to improve system robustness, accuracy, and profitability.What You'll Do:1. Data Engineering & Pipelines Build and maintain real-time and historical data pipelines for orders, trades, positions, and funds. Develop ETL workflows, ensuring unified metric and dimension definitions across systems. Own data consistency, quality, and latency for mission-critical risk processes.2. Real-Time Risk Monitoring & Control Develop highly visual risk dashboards by platform/product/account level. Implement risk order detection, anomaly detection, automated alerts, and circuit-breaker logic. Drive automated risk parameter tuning and safeguard mechanisms.3. Factor & Indicator Research Extract and validate risk and behaviour factors from historical data (e.g., markout, VPIN, volatility structure, flow imbalance). Integrate factors into risk models, market-making engines, routing policies, and evaluation frameworks.4. User Segmentation & Profiling Build classification models for retail vs. professional vs. arbitrage vs. HFT user types. Create risk grading models to improve strategy selection and routing optimization.5. PnL Attribution & Monitoring Build daily pipelines for PnL attribution covering fees, spread, funding, basis, and slippage. Run P&L anomaly detection and generate automated notifications.Core Skill: Strong SQL/Spark/Hive/Click House; data modeling, materialized views, performance tuning. BI tools: Fine BI, Power BI, Tableau. Understanding of: exposure, leverage, Greeks, hedge deviation, failure/latency rates, VaR/ES, TCA, plus factor backtesting metrics (IR, Sharpe, Max Drawdown). Scikit-learn: classification/regression, anomaly detection, feature engineering, drift monitoring. Familiarity with PyTorch / Tensor Flow. Spark/Flink, Kafka/Redpanda, Airflow/Dagster. Data quality frameworks such as Great Expectations. Proficient in Python/Java, with strong engineering and code quality practices.Preferred Qualifications Bachelor’s degree or above in Computer Science, Data Science, Financial Engineering, or related fields. Knowledge of risk control, anomaly detection, clustering, time-series analysis. Experience in derivatives, leveraged trading, FX/CFD, or multi-asset risk management. Strong risk sense with ability to connect models to business decisions. Excellent cross-functional communication with global teams (risk, trading, engineering).