VP, Quant Developer - Risk Analytics

Key Responsibilities

Design and implement end-to-end agentic workflows that enable autonomous planning, multi-step execution, and tool use across risk analytics and regulatory submission processes.
Architect and own the full system design of AI-powered risk platforms, including data flow, tool integration, orchestration layer, and production deployment.
Build and maintain validation frameworks and testing pipelines to ensure the correctness and reliability of AI-generated code and analytical outputs in a financial risk context.
Develop and integrate LLM-powered developer tooling - including CLI-based agents and code generation/review pipelines - to accelerate risk analytics delivery.
Collaborate with Market Risk, Credit Risk, and RegIM teams to translate complex domain requirements into robust, automated Python solutions.
Develop scalable, reusable Python libraries and contribute to a robust CI/CD environment through testing, peer review, and version control.
Facilitate efficient data processing, integration, and reporting pipelines to streamline regulatory submissions and internal analytics.

Required Qualifications

Education: Master's degree in Financial Engineering, Mathematics, Computer Science, or a related quantitative field preferred; Bachelor's considered with exceptional experience.
Experience: At least 5 years of professional experience in Python backend development for financial applications.
Demonstrated experience designing and maintaining multi-step agentic workflows, orchestration logic, branching execution, and tool use - not limited to simple LLM API calls.
Practical experience building validation frameworks or testing pipelines to verify AI-generated outputs in a professional setting; concrete examples required.
Hands-on experience with LLM-powered developer tooling or systems similar to Claude Code, including CLI-based AI agents or LLM-assisted code generation and review pipelines.
Proven ability to own full system architecture for agentic platforms, covering data flow, tool integration, orchestration, and deployment.
Strong knowledge of financial risk domains, specifically Market Risk (VaR, ES, Greeks, stress testing, FRTB) and Credit Risk (PD/LGD/EAD, CECL, CCAR, SA-CCR).
Demonstrated ability to develop scalable, reusable Python libraries.

Preferred Qualifications

Knowledge of RegIM / SIMM (Regulatory Initial Margin, ISDA SIMM methodology, IM regulatory submissions) is highly desirable.
Experience with portfolio analytics across multiple asset classes, including derivatives pricing and factor models.
Proficiency with DevOps tooling: Docker, Kubernetes, cloud platforms (Azure/AWS), and CI/CD pipelines.
Familiarity with graphical user interface (GUI) development in Python.
Ability to handle large datasets and implement efficient processing algorithms.