Senior Quant Developer

Project description Lead the design and development of advanced quantitative and AI-driven models for market abuse detection across multiple asset classes and trading venues. Drive the solutioning and delivery of large-scale surveillance systems in a global investment banking environment, leveraging Python, PySpark, big data technologies, and MS Copilot for model development, automation, and code quality. Play a pivotal role in communicating complex technical concepts through compelling storytelling, ensuring alignment, and understanding across business, compliance, and technology teams. Responsibilities - Architect and implement scalable AI/ML models (using MS Copilot, Python, PySpark, and other tools) for detecting market abuse patterns (e.g., spoofing, layering, insider trading) - across equities, fixed income, FX, and derivatives. - Collaborate closely with consultants, MAR monitoring teams, and technology stakeholders to gather requirements, share insights, and co-create innovative solutions. - Translate regulatory and business requirements into actionable technical designs, using storytelling to bridge gaps between technical and non-technical audiences. - Develop cross-venue monitoring solutions to aggregate, normalize, and analyze trading data from multiple exchanges and platforms using big data frameworks. - Design and optimize real-time and batch processing pipelines for large-scale market data ingestion and analysis. - Build statistical and machine learning models for anomaly detection, behavioral analytics, and alert generation. - Ensure solutions are compliant with global Market Abuse Regulations (MAR, MAD, MiFID II, Dodd-Frank, etc.). - Lead code reviews, mentor junior quants/developers, and establish best practices for model validation and software engineering, with a focus on AI-assisted development. - Integrate surveillance models with existing compliance platforms and workflow tools. - Conduct backtesting, scenario analysis, and performance benchmarking of surveillance models. - Document model logic, assumptions, and validation results for regulatory audits and internal governance. Skills Must have Technical Skills: - 7+ years of experience - Investment banking domain experience - Advanced AI/ML modelling (Python, PySpark, MS Copilot, kdb+/q, C++, Java) - Must be well versed with SQL and have hands on experience writing SQL (preferably Spark SQL) that is productionized (not ad-hoc queries) for at least 2-4 years - Familiarity with Cross-Product and Cross-Venue Surveillance Techniques particularly with vendors such as TradingHub, Steeleye, Nasdaq or NICE - Statistical analysis and anomaly detection - Large-scale data engineering and ETL pipeline development (Spark, Hadoop, or similar) - Market microstructure and trading strategy expertise - Experience with enterprise-grade surveillance systems in banking. - Integration of cross-product and cross-venue data sources - Regulatory compliance (MAR, MAD, MiFID II, Dodd-Frank) - Code quality, version control, and best practices. Soft Skills: - Strong storytelling and communication for technical and non-technical audiences - Collaboration with consultants, MAR monitoring teams, and technology stakeholders - Stakeholder management and requirements gathering - Leadership, mentoring, and team guidance - Problem-solving and critical thinking - Adaptability and continuous learning Nice to have - Understanding of Financial Markets Asset Classes (FX, FI, Equities, Rates, Commodities & Credit), various trade types (OTC, exchange traded, Spot, Forward, Swap, Options) and related systems is a plus - Surveillance domain knowledge, regulations (MAR, MIFID, CAT, Dodd Frank) and related Systems knowledge is certainly a plus