Senior Quant Developer

Project descriptionLead 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.SkillsMust haveTechnical 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 learningNice 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