2 months ago
London, United KingdomStaff+
Responsibilities
- Design, develop, and maintain Python-based services, pipelines, and tools supporting the quantitative model lifecycle across market and credit risk models.
- Build automated testing and validation frameworks with test orchestration, result capture, benchmarking, and reporting.
- Develop large-scale risk-data analysis and reconciliation tooling with data-quality, lineage, and traceability controls.
- Contribute to model inventory, workflow orchestration, approvals, periodic reviews, and audit-ready evidence generation.
- Implement and integrate AI/ML and LLM-based automation for model documentation, data-quality checks, and workflow assistance.
- Apply testing, monitoring, governance, secure design, and scalability controls to production AI tooling.
- Collaborate with quantitative analysts, model validators, data engineers, program leadership, and global cross-functional teams.
- Mentor junior developers and contribute to technical design reviews and engineering standards.
Requirements
- A STEM degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field is required; a Master's degree is preferred.
- Professional software development experience with deep expertise in Python and its data and engineering ecosystem, including tools such as pandas, NumPy, and FastAPI.
- Demonstrated delivery of production-grade automation, data pipelines, or testing frameworks in a complex enterprise environment.
- Practical AI/ML experience, including machine learning libraries or LLM application development such as API integration, prompt engineering, and RAG-style document workflows.
- Strong experience with test automation, CI/CD, Git, code review, containerization, and modern software engineering practices.
- Experience with large datasets, data-quality and lineage checks, SQL, and enterprise data platforms.
- Ability to collaborate globally and explain technical concepts to non-technical stakeholders.
- Financial services, quantitative risk models, model lifecycle governance, banking regulation, cloud environments, workflow orchestration, Docker, Kubernetes, and AWS or Google Cloud experience are preferred.
- Background in quantitative finance, statistics, or data science, an advanced quantitative degree, and experience mentoring engineers or leading technical workstreams are preferred.
Categories
About Citi
Citi is a public financial-services company offering consumer and institutional banking, credit cards, wealth management, treasury and trade solutions, and capital-markets services. It serves individuals, corporations, financial institutions, and governments in more than 160 countries and jurisdictions, earning interest and fee income from lending, payments, trading, and advisory. Founded in 1812 and headquartered in New York, it trades on the NYSE under the ticker C.