Quantitative Analyst

Company Description

BTC Dana is a leading next-generation financial brokerage firm based in Dubai, dedicated to revolutionizing the investment landscape. We offer a diverse range of over 500 CFD trading instruments, including FX, Cryptocurrencies, Commodities, and Stocks. Our user-friendly FinTech-designed platform and dedicated customer support make trading easy and enjoyable for everyone, ensuring transparency and fund safety.

Role Description

This is a full-time on-site role for a Quantitative Analyst (Risk), located in Dubai. The Quantitative Analyst will be responsible for developing and implementing quantitative models to analyze market risks, conducting statistical analysis, and supporting the trading platform's risk management strategies. Daily tasks will include data analysis, designing risk metrics, collaborating with various teams to optimize trading strategies.

Key Responsibilities:

• Factor & Indicator Mining: Build risk/behavioural factors from historical data (e.g., markout, VPIN), integrated with risk management.
• User Segmentation & Profiling: Classification models for retail/professional/arbitrage/HFT clients, with risk grading to drive strategy and routing optimization.
• PnL Attribution: Daily profit and return attribution (fees, spread, funding, basis, slippage) pipelines and anomaly notifications.
• Data Pipelines (Historical/Real-time): ETL of orders, trades, positions, and funds; unified definitions of metrics and dimensions.
• Real-time Risk Control & Alerts: Dashboards (by platform/product/account), risk order detection, anomaly detection, automated risk parameter adjustment, and circuit-breaker recommendations.

Core Skills:

• Risk Metrics: Exposure, leverage, Greeks, hedge deviation, latency/failure rate, VaR/ES, TCA, risk factor backtesting metrics (IR, Sharpe, Max Drawdown).
• Machine Learning: Scikit-learn; classification/regression, anomaly & change-point detection, feature engineering, model drift/calibration & monitoring; familiarity with PyTorch/TF.
• Databases & Tools: SQL/Spark/Hive/ClickHouse, data modeling/partitioning/materialized views, BI tools (FineBI/PowerBI/Tableau).
• Data Stream Processing: Spark/Flink, Kafka/Redpanda, Airflow/Dagster; data quality frameworks (Great Expectations).
• Programming: Python/Java, engineering practices (testing, logging, version control).
• Cloud platform: Google Cloud Platform is preferred

Preferred Qualifications:

• Bachelor’s degree or above in Computer Science, Financial Engineering, Data Science, or related fields.
• Familiarity with common risk control and anomaly detection algorithms (supervised/unsupervised learning, clustering, time series analysis).
• Prior experience in financial derivatives, leveraged trading, or FX/CFD risk management preferred.
• Strong risk awareness, with ability to connect models to business use cases.
• Strong cross-functional communication skills (collaborating with risk, trading, and development teams).