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 :

Soft Skills :

Nice to have