Senior Python Developer - Quant Models AI Automation, Vice President

Key Responsibilities

Engineering & Delivery

• Design, develop, and maintain Python-based services, pipelines, and tools supporting automation of the quantitative model lifecycle across market risk and credit risk model families.
• Build and operate automated testing and validation frameworks for quantitative models, including test orchestration, result capture, benchmarking, and reporting.
• Develop data analysis and reconciliation tooling over large-scale risk datasets, ensuring data quality, lineage, and traceability across risk platforms.
• Contribute to model lifecycle management tooling: model inventory, workflow orchestration, approvals, periodic reviews, and audit-ready evidence generation.

AI Enablement

• Implement AI/ML components within the model workflow, including LLM-based automation for model documentation generation and updating, intelligent data quality checks, and workflow assistance.
• Integrate AI tooling into a controlled, auditable, production-grade environment, with appropriate testing, monitoring, and governance controls.
• Stay current with developments in applied AI/ML and evaluate their applicability to risk technology use cases.

Collaboration & Standards

• Work within a cross-functional agile team alongside quants, validators, data engineers, and program management.
• Promote engineering best practices: code quality, testing, CI/CD, documentation, and secure, scalable design.
• Mentor junior developers and contribute to technical design reviews.

Required Qualifications

• STEM degree (Computer Science, Engineering, Mathematics, Statistics, Physics, or related); Master's degree preferred.
• Professional software development experience, with deep expertise in Python and its data/engineering ecosystem (e.g., pandas, NumPy, FastAPI, orchestration tools).
• Proven track record of delivering production-grade automation, data pipelines, or testing frameworks in a complex enterprise environment; financial services experience strongly preferred.
• Solid AI/ML knowledge, including practical experience with machine learning libraries and/or LLM-based application development (e.g., API integration, prompt engineering, RAG-style document workflows).
• Strong experience with test automation, CI/CD, and modern software engineering practices (Git, code review, containerization).
• Experience working with large datasets, data quality/linearity checks, and SQL; familiarity with enterprise data platforms.
• Ability to work effectively in a cross-functional, global team and communicate technical concepts to non-technical stakeholders.

Preferred Qualifications

• Exposure to quantitative risk models (market risk and/or credit risk) and the model lifecycle: development, validation, documentation, and ongoing monitoring.
• Familiarity with the model risk regulatory landscape and governance expectations in banking.
• Experience with workflow orchestration platforms, cloud environments (AWS/Google Cloud), and container orchestration (Docker/Kubernetes).
• Background in quantitative finance, statistics, or data science; an advanced quantitative degree is a plus.
• Experience mentoring engineers and leading small technical workstreams.

Job Family Group

Technology

Job Family

Applications Development

Time Type

Full time

Most Relevant Skills

Please see the requirements listed above.

Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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