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BNY · 1 day ago

Vice President, Model Risk Management

New York, NY

Full-time

Onsite

Mid Level

$68K/yr - $130K/yr

2+ years exp

BNY is a leading global financial services company that influences nearly 20% of the world’s investible assets. They are seeking a Vice President for their Model Risk Management team, responsible for executing enterprise standards for model validation and evaluating the strengths and weaknesses of various models. The role requires a strong quantitative and analytical background along with experience in complex quantitative modeling.

Financial Services

Responsibilities

Execute enterprise standards for model validation, by setting the scope of a validation effort. This entails designing the tests and review activities necessary to evaluate a model

Evaluate the strengths and weaknesses of a model's conceptual framework to identify situations where a model may become less useful

Reviews accuracy of reports and calculations performed by less experienced colleagues

Responsible for the technical direction, accuracy, and soundness of quantitative methods in the assigned area. Decisions and assumptions recommended by the incumbent have significant impact on the financial and risk position of the Bank or legal entity supported

Complete required training, disclosures, attestations, and certifications within deadlines

Qualification

Quantitative analysisComplex quantitative modelingProgramming languagesFinancial services experienceMathematical/statistical softwareDocumentation skillsCommunication skills

Required

Master's Degree or PhD in a quantitative discipline, including engineering, mathematics, physics, statistics, econometrics

The candidate must have a superb quantitative and analytical background with a solid theoretical foundation coupled with strong programming, documentation, and communications skills

Minimum 2 years (2 - 5 preferred) of modelling experience in financial services

Must have experience with complex quantitative modelling, numerical analysis, and computational methods using programming languages (such as C/C++, C#, Java, FORTRAN, MATLAB, SAS) as well as mathematical/statistical software packages