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Corporate - Model Risk Governance Review - Model Review Group - VP

Req #: 170083736
Location: New York, NY, US
Job Category: Accounting/Finance/Audit/Risk
Potential Referral Amount: 5000 US Dollar (USD)
  • JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2 trillion and operations in more than 60 countries. The firm is a leader in investment banking, financial services for consumers, small business and commercial banking, financial transaction processing, asset management, and private equity.
  • The Model Risk Group (MRG) is responsible for conducting model validation to help identify, measure, and mitigate Model Risk. In particular, the group reviews economic, regulatory capital and loss forecasting models, analyses complex model risk, and assesses appropriateness of risk measurement and reserve methodologies across major lines of business (Mortgage Bank, Card Services, Auto and Student lending, Investment Bank, Chief Investment Office, Commercial Bank, Asset Management, etc.). The objective is to ensure that models are used appropriately in the business context and that model users are aware of the models’ strengths and limitations and how these can impact their decisions.
  • Retail and Wholesale credit products (e.g. mortgages, credit cards, auto loans, institutional loans and related credit products) are a core to US Financial Institutions, involving extensive use of a variety of models: scoring, rating and pricing models at origination; financial forecast, risk measurement and hedging models applied to the risk inventory resulting from origination and servicing and the trading of MBS/ABS. Sound usage of the models requires a deep understanding of the product and marketplace and of the forecast models’ ability to predict consumer behavior and supply and demand of credit under different economic scenarios.
  • MRG carries out model validation activities and works closely with Risk, Finance and LOB professionals to review findings, on-going model risk measurement and risk mitigating strategies.

Core responsibilities:

  • Lead the validation of Credit Risk Capital parameter estimation (RWA) models for whole loans and structured products across the Retail and Wholesale Lines of Business, and Loss Forecasting (CCAR, ICAAP, ALLL) models for Wholesale credit products in the Firm’s C&I portfolio.
  • Carry out model validation, including model reviews and model risk measurement:
    • Model reviews: evaluate conceptual soundness of model specification; reasonableness of assumptions and reliability of inputs; completeness of testing performed to support the correctness of the implementation; robustness of numerical aspects; suitability and comprehensiveness of performance metrics and risk measures associated with use of model.
    • Model risk measurement: design and implement experiments to measure the potential impact of model limitations, parameter estimation error or deviations from model assumptions; compare model outputs with empirical evidence and/or outputs from model benchmarks.
  • Motivate staff and effectively manage business priorities to complete high quality model reviews.
  • Ability to operate independently with minimum oversight and manage multiple deliverables in parallel.
    Liaise with the Lines of Business, Finance and Risk professionals and lead regulatory engagements.
 
  • PhD or MS degree in Applied Mathematics, Finance, Statistics, Physics, Engineering or similar.
  • 6-10 years of development and/or validation experience with empirical models for Retail and/or Wholesale products.
  • Deep understanding of probability theory, econometrics, statistics, and numerical methods.
  • Excellent analytical and problem solving abilities.
  • Excellent communication skills – both written and oral.
  • Ability to ask right questions, identify the “big picture” risks and escalate issues. Risk & Control mindset
  • Familiarity with SR 11-07, Basel rules and the broader regulatory environment are a plus.
  • Experience with programming languages/statistical software (e.g., SAS, Python, R) is highly desirable.
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