AVP - Model Risk Management - BFSI

Criteria:

- Solid understanding of Mathematical Models, Probability: Stochastic Processes, Simulations, Monte Carlo methods etc., Statistics: Parametric and non-Parametric Statistics, Statistical Tests, Bootstrap, Time Series Modelling etc.

- Programming capability in R, Matlab, SAS, C++, VBA, SQL will be highly appreciated.

- Understanding of investment banking products: Derivatives Products traded on exchanges and OTC

- Solid understanding of arbitrage-free pricing(Black-Scholes framework etc.), Capital Asset Pricing Model and other theoretical aspects of Finance

- Strong understanding of regulatory landscape( Basel 2-3, CRD IV, SR-11 etc.)

Responsibilities:

The successful candidate will:

- Be expected to lead and manage independent validation reviews across a wide range of core Risk Capital and CCAR models used throughout the bank, meeting business needs and regulatory expectations, with responsibility for investigating key aspects of each model under review: choice of modelling approach, the underlying assumptions and associated limitations, performance and optimal use of the model, etc.

- Review, verify and validate risk models for theoretical soundness, testing design and identification of model weaknesses, ensuring ongoing monitoring, as well as contribute in the firm-wide model risk and control assessment.

- Be expected to demonstrate independence in planning and stakeholder engagement, testing design and execution, results interpretation and presentation, and the production of documentation strong enough to evidence a sound challenge to both internal and external parties.

- Create model risk reports, including authoring and presentation of validation reports for the attention of senior management, supervisory authorities, and model stakeholders

The truly global scope of model risk means that this role will involve working with an incredibly broad group of stakeholders from every part of the firm, investigating model risk and model governance standards and performing detailed validation of risk models