Model Risk Governance VP

Confidential Company  •  New York, NY

15+ years experience  •  Technology

Salary depends on experience
Posted on 06/22/18 by Radhika Arora
Confidential Company
New York, NY
15+ years experience
Technology
Salary depends on experience
Posted on 06/22/18 Radhika Arora

A leading global financial services firm is seeking a Model Risk Governance VP to join their team in New York. This candidate will join a group that oversees model risk, conducts independent model reviews and provides guidance around a model's appropriate usage. This group has recently started a Machine Learning Center of Excellence to ensure that risks posed by ML models are captured accurately and consistently across various Lines of Business. Such models are currently used by the bank to detect fraud, improve marketing techniques, and optimize order routing in markets, among other things.

Responsibilities:

  • Evaluate conceptual soundness of model techniques, specifications and feature sets; reasonableness of assumptions; reliability of inputs; completeness of testing performed; correctness of implementation; and suitability / comprehensiveness of performance metrics and risk measures.
  • Measure the potential impact of model limitations, convergence errors or deviations from model assumptions; compare model outputs with empirical evidence and/or outputs from benchmark models.
  • Evaluate the risk posed by non-transparency and non-linearity in ML models, and suggest ways to mitigate such risks.
  • Liaise with Developers, Finance, and Risk professionals to monitor usage and performance of the models.
  • Evaluate econometric and mathematical models developed by the office of the Chief Investment Officer (CIO), and various other lines of business such as the retail bank, commercial bank and investment bank.

Skills:

  • Ph.D. or master's degree in a quantitative field such as Finance, Economics, Math, Physics or Engineering is required.
  • 10+ years relevant experience
  • Experience in development of AI / ML models and use of large data sets is required.
  • Understanding of statistics / econometrics.
  • Thorough knowledge of at least one programming language such as Matlab, R, Python, C/C++, etc.
  • Good communication skills

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