Quant Modeling Lead Python

About the Role

As a Python Developer within the Wholesale Credit Quantitative Research Core team you will play a central role in building and maintaining Nova the firms strategic platform for Loan Loss Forecasting models. Nova is built within JPMorgans Athena platform and underpins critical regulatory and business processes including CECL IFRS 9 CCAR ICAAP and Risk Appetite forecasting.

You will be responsible for designing and implementing the core frameworks and libraries that model developers rely on to build test and deploy forecasting models at scale. This is a hands-on engineering role that demands strong software craftsmanship quantitative aptitude and the ability to translate partially defined business needs into robust production-quality systems. You will work closely with quantitative researchers model governance technology partners and wholesale credit business stakeholders.

Job Responsibilities

• Design build and maintain the core Python frameworks and libraries that power Nova ensuring they are performant extensible and easy for model developers to integrate with
• Develop and enhance the calculation engine and related tooling for loan loss forecasting models supporting CECL IFRS 9 CCAR ICAAP and Risk Appetite requirements
• Implement high-performance numerical algorithms using Python scientific computing libraries including NumPy Pandas and DuckDB
• Champion test-driven development practices across the team building and maintaining comprehensive unit integration and regression test suites to ensure framework reliability
• Take partially specified problems and business needs from stakeholders and translate them into concrete technical requirements designs and implementation plans
• Leverage LLM-based coding tools (e.g. GitHub Copilot Claude) to accelerate development velocity drive code quality and maximize team productivity
• Perform peer code reviews with a focus on correctness performance maintainability and adherence to team standards
• Prepare clear and thorough technical documentation covering design decisions implementation details and testing strategies
• Partner with model developers product and business stakeholders during the implementation testing and operationalization of forecasting processes
• Present regular updates on development progress technical decisions and platform roadmap to senior management and cross-functional stakeholders
• Investigate and debug counter-intuitive observations in model forecasts performing root-cause analysis at the framework and data level

Required Qualifications Capabilities and Skills

• Bachelors or Masters degree in Computer Science Mathematics Physics Engineering or a related quantitative discipline
• Minimum 5 years of experience in quantitative software development within a financial services environment (e.g. banking asset management hedge fund fintech)
• Advanced proficiency in Python with deep experience in object-oriented design design patterns and building production-grade frameworks and libraries
• Strong working knowledge of NumPy and Pandas for numerical computing and data manipulation
• Demonstrated experience with test-driven development and building systems with rigorous unit integration and regression test coverage (e.g. pytest unittest)
• Strong analytical quantitative and problem-solving skills with the ability to reason about complex model behavior and data flows
• Excellent written and verbal communication skills with the confidence to present technical concepts to both technical and non-technical audiences
• Proven ability to operate as a self-starter: taking ambiguous or partially specified problems and driving them through to well-defined technical solutions
• Proficiency with LLM-based coding tools and a track record of leveraging AI assistants to meaningfully increase development productivity and code quality

Preferred Qualifications Capabilities and Skills

• Experience with DuckDB or similar in-process analytical databases for high-performance data querying and transformation
• Knowledge of credit risk concepts including Wholesale Credit CCAR/DFAST stress testing CECL/IFRS 9 allowance and Basel III regulatory capital
• Experience working with or building upon large-scale analytics platforms (e.g. JPMorgan Athena or comparable quantitative computing environments)
• Familiarity with distributed computing frameworks and techniques for scaling numerical workloads
• Knowledge of statistical modeling Monte Carlo simulation and time-series forecasting methodologies
• Ability to work effectively with large datasets and practical knowledge of SQL and database systems
• Proven ability to build collaborative relationships with cross-functional partners including model developers business stakeholders and technology teams