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Corporate - Model Risk Governance & Review - Model Review Group - Associate

JP Morgan Chase – Jersey City, NJ

JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2.7 trillion and operations worldwide. The firm is a leader in investment banking, financial services for consumers and small business, commercial banking, financial transaction processing, and asset management. A component of the Dow Jones Industrial Average, JPMorgan Chase & Co. serves millions of consumers in the United States and many of the worlds most prominent corporate, institutional and government clients under its J.P. Morgan and Chase brands.

Associate role in the Model Review Group (MRG) of JPMorgan (Machine-learning model specific)

About the Group

The Model Risk Governance and Review Group (MRGR) oversees model risk at JPMorgan, conducts independent model reviews and provides guidance around a models appropriate usage. The Model Review Group (MRG) is part of MRGR and is tasked with assessing and mitigating the risk posed by usage of all types of quantitative models in the firm.

About the Team

Our team oversees the risk posed by models in the field of Artificial Intelligence (AI) and Machine Learning (ML). We are data scientists, risk managers, and early adopters of technology tasked with mitigating risk from AI/ML solutions while, at the same time, providing guidance around the appropriate development of such models.

Why Join Us?

We offer a dynamic environment for professional development in the emerging field of AI and ML. In addition we also offer you the opportunity to:

  • Solve challenging and ambiguous ML/AI problems and publish your solutions as guidelines to model developers across various functions and lines of business.
  • Review a large variety of ML and AI applications in banking, allowing you to learn in a real-world environment.
  • Get access to experts, state of the art infrastructure and deep scientific knowledge base built up over several decades of work.

About the Role

The Associate level position requires the successful candidate to 1) quantitatively evaluate complex AI / ML models, and 2) build benchmark models in the process of evaluating (1).

A significant portion of the successful candidates time is likely to be spent on reviewing AI / ML models. Such models are currently used to detect fraud, improve marketing techniques, and optimize order routing in markets, amongst other applications.

The position is located in New York, NY.

Core responsibilities

  • Evaluate conceptual soundness of AI and ML model specification; reasonableness of assumptions; reliability of inputs; completeness of testing performed; correctness of implementation; and suitability / comprehensiveness of performance metrics and risk measures.
  • Design and implement experiments to measure the potential impact of model limitations, parameter estimation errors or deviations from model assumptions; compare model outputs with empirical evidence and/or outputs from model benchmarks.
  • Evaluate the risk posed specifically by non-transparent and non-linear models, and suggest ways to mitigate such risks.
  • Liaise with front office, Finance and Risk professionals to monitor usage and performance of the models.
  • Evaluate market conditions under which a given model is likely to break down.
  • Identify market risks most relevant to the banks various lines of business.
  • Cogently document findings.

Desirable skills, experience, and qualifications

  • A Ph.D. or masters degree in a quantitative field such as Finance, Economics, Math, Physics or Engineering is required
  • 0-3 years of experience. Candidates with significantly more experience may be considered for more senior roles.
  • The candidate is expected to have a good understanding of machine learning models. Experience with large data sets and training ML models is required.
  • Understanding of statistics / econometrics
  • Thorough knowledge of at least one programming language such as Matlab, R, Python, C/C++, etc. is required
  • Communication skills are important since the role requires interacting with many groups across the firm as well as producing documents for both internal and external (regulatory) consumption

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