Quant Modeling Counterparty Credit Risk Vice President

Greater London
Full time
Posted
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JP Morgan
Banking, investment & finance
10,001+ employees
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Model Risk Governance and Review (MRGR) is a global team of modeling experts within the firm’s Risk Management and Compliance organization. The team is responsible for conducting independent model review and model governance activities to help identify, measure, and mitigate Model Risk in the firm. Within MRGR, the MRGR Counterparty Credit Risk (CCR) team manages model risks of XVA and Counterparty Credit Risk models for the vast derivatives portfolios at JPMorgan.

As a Quant Modeling Counterparty Credit Risk Vice within MRGR Credit Portfolio team, you will lead the development and expansion of benchmarking library and related tools to enhance the model validation team’s ability to carry out independent testing activities for models used in CCR space. You will leverage your technical expertise and intellectual rigor to identify and assess model risks of various component models (risk factor simulation engines and derivatives pricing models) across different asset classes, collateral and exposure aggregation models specific to CCR space, as well as different types of end-usage models such as Credit and Funding Valuation Adjustment (collectively known as XVA) for fair valuation, Potential Future Exposure for credit risk management and Regulatory Exposure for capital calculation.

Job responsibilities

  • Leads the development of CCR benchmarking library and independent testing tools, ensuring scalability, performance, reusability and compatibility with existing systems.
  • Coordinates the work of a diverse team of contributors and development work streams, providing technical guidance to team members and help in integrating their components.
  • Performs regular code reviews, promotes and maintains the standards of core library, ensures it follows best practices for development, testing coverage and change control.
  • Contributes to model review projects, which involves evaluation of the conceptual soundness of the models, the adequacy of the testing to support the model assumptions and the correctness of the implementation, providing a challenge to quantifications of model limitations, assessing the suitability and comprehensiveness of performance metrics.
  • Designs and implements experiments to explore various aspects of model risk, including construction of relevant benchmarks, measurement of model performance in context of actual or hypothetical positions, identification of conditions with heightened model risk, performing verification activities of testing conducted by model developers.
  • Researches the literature for latest developments and techniques employed in the space and identifies promising areas to extend library’s capabilities and enhance compute performance.
  • Liaises with FO, Quants, Counterparty Credit Risk management, Finance and Valuation Control groups to understand the business usage, areas of interest for deep-dives as well as to communicate the findings of model reviews and independent testing activities.

Required qualifications, capabilities, and skills

  • Solid experience in developing and implementing complex models as well as conducting performance analysis in a quantitative research or model review function
  • PhD or MS degree in Math, Math Finance, Physics, Computer Science, Engineering or similar
  • Advanced Python proficiency and in-depth knowledge of packages designed for HPC, profiling tools and memory management
  • Good understanding of parallel computing architectures, experience in developing and optimizing code to run on multi-core processors, GPUs and distributed environments
  • Strong knowledge of numerical methods used in calibration, optimization and Monte Carlo simulation and experience with reducing computational complexity
  • Strong communication skills and team player mind-set
  • Inquisitive nature, ability to ask right questions and escalate issues

Preferred qualifications, capabilities, and skills

  • Familiarity with Counterparty Credit Risk space and relevant models
  • Experience of working with tensorflow and other ML packages