Vice President of Quantitative Modeling for Counterparty Credit Risk

3 weeks ago

London, Greater London, United Kingdom JP Morgan Chase Bank, National Association Full time
Position Overview

The Model Risk Governance and Review (MRGR) team is a global collective of modeling specialists within the Risk Management and Compliance division. This team is tasked with executing independent evaluations and governance functions to identify, quantify, and mitigate Model Risk across the organization. Within MRGR, the Counterparty Credit Risk (CCR) team is responsible for overseeing model risks associated with XVA and Counterparty Credit Risk models for extensive derivatives portfolios at J.P. Morgan Chase Bank.

As a Vice President of Quantitative Modeling for Counterparty Credit Risk within the MRGR Credit Portfolio team, you will spearhead the creation and enhancement of a benchmarking library and associated tools to bolster the model validation team's capacity for independent testing of models utilized in the CCR domain. Your technical acumen and analytical rigor will be pivotal in identifying and evaluating model risks across various component models, including risk factor simulation engines and derivatives pricing models across diverse asset classes, as well as collateral and exposure aggregation models pertinent to the CCR space. You will also assess various end-usage models such as Credit and Funding Valuation Adjustment (collectively referred to as XVA) for fair valuation, Potential Future Exposure for credit risk management, and Regulatory Exposure for capital computation.

Key Responsibilities
  • Lead the development of the CCR benchmarking library and independent testing tools, ensuring scalability, performance, reusability, and compatibility with existing systems.
  • Coordinate the efforts of a diverse team of contributors and development work streams, providing technical guidance and assisting in the integration of their components.
  • Conduct regular code reviews, uphold and maintain the standards of the core library, and ensure adherence to best practices for development, testing coverage, and change control.
  • Contribute to model review initiatives, evaluating the conceptual integrity of models, the adequacy of testing to support model assumptions, and the accuracy of implementations, while challenging quantifications of model limitations and assessing the suitability and comprehensiveness of performance metrics.
  • Design and execute experiments to investigate various facets of model risk, including the construction of relevant benchmarks, measurement of model performance in real or hypothetical scenarios, and identification of conditions with heightened model risk, along with verification activities of testing conducted by model developers.
  • Research current literature for the latest advancements and techniques in the field, identifying promising areas to enhance the library's capabilities and improve computational performance.
  • Engage with Front Office, Quants, Counterparty Credit Risk management, Finance, and Valuation Control teams to comprehend business applications, areas of interest for in-depth analysis, and to communicate findings from model reviews and independent testing activities.
Required Qualifications and Skills
  • Extensive experience in developing and implementing complex models and conducting performance analysis within a quantitative research or model review context.
  • PhD or MS degree in Mathematics, Mathematical Finance, Physics, Computer Science, Engineering, or a related field.
  • Advanced proficiency in Python and comprehensive knowledge of high-performance computing packages, profiling tools, and memory management.
  • Strong understanding of parallel computing architectures, with experience in developing and optimizing code for multi-core processors, GPUs, and distributed environments.
  • Deep knowledge of numerical methods employed in calibration, optimization, and Monte Carlo simulation, with experience in reducing computational complexity.
  • Excellent communication skills and a collaborative mindset.
  • Curious disposition, with the ability to ask pertinent questions and escalate issues as necessary.
Preferred Qualifications
  • Familiarity with the Counterparty Credit Risk domain and relevant models.
  • Experience working with TensorFlow and other machine learning frameworks.
About J.P. Morgan

J.P. Morgan is a leading global financial services firm, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals, and institutional investors. Our commitment to serving clients with excellence drives everything we do. We aim to build trusted, long-term partnerships to assist our clients in achieving their business goals.

We recognize that our employees are our greatest asset, and the diverse talents they contribute to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion within our organization. We do not discriminate based on any protected attribute and are committed to providing reasonable accommodations for applicants' and employees' needs.

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