Join our Regulatory & Financial Risk team as a Deputy Manager specializing in Credit Risk Quantitative Analysis. This role is pivotal in enhancing our risk management capabilities. You will focus on developing and validating advanced models critical for financial markets.

We are seeking a skilled professional to contribute to model development and validation, specifically focusing on Market Risk (FRTB IMA, SA and Basel 2.5), Counterparty Credit Risk, and Pricing models. A strong background in statistical modeling, quantitative research, and market risk management is essential.

This position involves conducting in-depth, independent reviews and validation of various critical risk models.

Key responsibilities include assessing Market Risk Models (FRTB – IMA & SA, Basel 2.5, VaR, Expected Shortfall, Sensitivities), Counterparty Credit Risk Models (IMM, internal exposure models like EPE, EE, PFE, XVA models, Margin models like DIM, SIMM), and Stress Testing Models (CCAR, DFAST, ICAAP).

Furthermore, you will evaluate model inputs, outputs, and assumptions, assess conceptual soundness and methodological appropriateness, and perform performance testing, benchmarking, sensitivity analysis, and back-testing. You will also review model implementation, identify potential risks, and prepare comprehensive validation and model risk reports adhering to regulatory standards like SR 11-7, ECB, and PRA.

We are looking for candidates with a Master’s degree in a quantitative field such as finance, financial mathematics, statistics, economics, engineering, or an MBA, coupled with 5-8 years of relevant professional experience.

Ideal candidates will possess sound knowledge and 2-7 years of experience in areas like Market Risk models (VaR, RNIV, IRC, SA-CVA, FRTB models), Stress testing and Capital models (CCAR, DFAST, ICAAP), or Counterparty Credit Risk (SACCR, XVA, margin models, exposure models).

A strong grasp of Market Risk metrics (VaR, Expected Shortfall, sensitivities, FRTB, back-testing) and CCR metrics (EPE, EE, PFE, EAD, SA-CCR, margin models) is crucial. You should also have a solid understanding of stochastic calculus, probability theory, numerical methods, statistical techniques, stress testing, scenario analysis, risk management strategies, and derivatives across various asset classes.