Sr. Model Validation Analyst

  • 37082
  • Non-Life - Data Science
  • |
  • Michigan, United States
  • |
  • Mar 19, 2020
  • Conducts model validation to help identify, measure, and mitigate Model Risk.
  • Reviews models, analyses complex model risk, and assesses the appropriateness of risk measurement and reserve methodologies across lines of business.
  • Ensures 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.
  • Evaluates 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 the use of the model.
  • Performs 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.
  • Verifies the proper execution of the model.
  • Conducts reviews in accordance with supervisory guidance on model risk management as well as other applicable internal and regulatory guidelines and procedures.
  • Keeps updated with and utilizes industry best practices and advanced modeling techniques, supplemented by expert judgment and qualitative evaluation, to facilitate a program of validation and independent review that meets the requirements and framework as defined by regulation and policy to provide a credible independent challenge.
  • Able to relate complex financial theory and models to the changing economic environment, and must be able to understand the limitations of models based on historical experience.
  • Ability to operate independently with minimal oversight and manage multiple deliverables in parallel.
  • Ability to liaise with the Lines of Business, Finance and Risk professionals and lead regulatory engagements.
  • Complete other responsibilities as assigned.
  • Master’s degree in business, mathematics, statistics, industrial engineering, or operations research related fields
  • Minimum of 6 years of development and/or validation experience with empirical models
  • Familiarity with SR 11-07, Basel rules and the broader regulatory environment are a plus
  • Deep understanding of probability theory, econometrics, statistics, and numerical methods.
  • Analytical and problem-solving abilities.
  • Communication skills – both written and oral.
  • Ability to ask the right questions, identify the “big picture” risks and escalate issues.
  • Experience with one or more programming languages/statistical (e.g. SAS, Python, R) software.
  • Knowledge of complex financial risk modeling and analysis
  • Risk & Control mindset