Join our dynamic team as a Market Risk Quant in Hyderabad, specializing in model development and validation within Regulatory & Financial Risk. This role is crucial for enhancing our capabilities in market risk, counterparty credit risk, and pricing models. You will be instrumental in developing, reviewing, and validating sophisticated financial models.

We are seeking a candidate with a robust background in statistical modeling, quantitative research, stochastic calculus, and market risk management. Your expertise will directly contribute to mitigating risks and identifying opportunities, ensuring our clients are well-protected and strategically positioned.

Your primary responsibilities will include conducting independent reviews and validations of critical models. This encompasses Market Risk models (FRTB – IMA & SA, Basel 2.5, VaR, Expected Shortfall, Sensitivities), Counterparty Credit Risk models (IMM/internal exposure, EPE, EE, PFE, XVA, Margin models), and Stress Testing models (CCAR, DFAST, ICAAP).

You will meticulously assess model inputs, outputs, and assumptions, evaluating their conceptual soundness and methodological appropriateness. Performing performance testing, benchmarking, sensitivity analysis, and back-testing are key aspects of this role. Additionally, you will review model implementation, identify potential risks, and recommend remediation actions, preparing comprehensive reports aligned with regulatory standards.

We are looking for candidates holding a Master's degree in a quantitative field such as finance, financial mathematics, statistics, economics, or engineering, or an MBA. A minimum of 5 to 8 years of relevant professional experience is required.

Demonstrated knowledge and practical experience (2-7 years) in Market Risk models (VaR, RNIV, IRC, SA-CVA, FRTB SA/IMA), Stress testing and Capital models (CCAR, DFAST, ICAAP), or Counterparty Credit Risk (SACCR, XVA, Risk factor simulation, Margin/Exposure models) are essential. Strong foundational understanding of market and counterparty credit risk metrics, stochastic calculus, probability theory, numerical methods, statistical tests, and risk management strategies is expected. Familiarity with derivatives and financial instruments across various asset classes is also important.