Quantitative Leader

3 weeks ago

London, Greater London, United Kingdom undisclosed Full time
About the Role

We are seeking a highly skilled Quant Modeling Counterparty Credit Risk Vice to join our MRGR Credit Portfolio team. As a Quant Modeling Counterparty Credit Risk Vice, 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.

Key Responsibilities
  • Lead the development of CCR benchmarking library and independent testing tools, ensuring scalability, performance, reusability and compatibility with existing systems.
  • Coordinate the work of a diverse team of contributors and development work streams, providing technical guidance to team members and help in integrating their components.
  • Perform regular code reviews, promote and maintain the standards of core library, ensures it follows best practices for development, testing coverage and change control.
  • Contribute 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.
  • Design and implement 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.
  • Research the literature for latest developments and techniques employed in the space and identifies promising areas to extend library's capabilities and enhance compute performance.
  • Liaise 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.
Requirements
  • 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.

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