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/ClickHouse; data modeling, materialized views, performance tuning.
• BI tools: FineBI, PowerBI, 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 / TensorFlow.
• 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).