Vice President, Model Risk Management
This position reports to the Head of Model Risk Management and provides independent oversight and effective challenge over models the Bank relies on for business management, regulatory capital, and stress testing. In particular, this individual is responsible for independently validating the conceptual and implementation soundness of internally and externally developed models, as well monitoring compliance with model governance (performance monitoring, sensitivity analysis, and processes and data controls).
Effective challenge as second line defense over quantitative models developed internally and externally, covering: documentation, justification of model construct, model assumptions, testing, weaknesses and limitations, implementation, model design, model controls, input data verification and model performance monitoring. Model risk management governance, including periodic reviews, management of model inventory, model change management, model classification and rating, and timely resolution of validation, audit and Regulatory findings.
Interact and build relationships with model developers, owners and users.
Justify the reasoning behind validation findings and address concerns from regulators and internal audit.
Non-model risk management governance, including non-model inventory, non-model change management, and risk-based non-model approvals.
Conduct independent research related to best practices in model development and design, as well as efficient data management and warehousing.
Graduate degree in a quantitative field, including Economics, Finance, Mathematics, Statistics or related field.
Ten  years of experience in quantitative modeling.
Good programming skills (R, Matlab, Python, etc.).
Prior experience developing or validating models in the following areas is a plus: Credit risk (CRE, Residential, Consumer and Industrial, Retail), BSA/AML, PPNR, NII/EVE, Liquidity, ALLL/CECL,Stress testing.
Good verbal and communication skills.
Prior exposure to Artificial Intelligence and Machine Learning models is also a plus.