Senior Quant Developer

Project description- We need a Senior Quant Developer to work for a leading investment bank client for the part of Trade Surveillance Team. 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