Assistant Manager - Measurement and Verification

You will be responsible for conducting model validation, audit, and review primarily for credit loss forecasting models in the retail or wholesale domain, specifically for IFRS9/IRB models, including PD/EAD/LGD components. Your role will involve comprehending relevant regulatory requirements, reviewing development documentation, conducting tests and benchmarking, as well as creating challenger models using SAS, R, or Python, and compiling reports. Additionally, you will provide assistance with other model validation, audit, and review tasks as needed, such as Underwriting scorecards, Credit Scoring, behavioral models, economic scenario models, or validation-related automation activities. Your responsibilities will include performing model validation by assessing conceptual soundness, critically evaluating the testing conducted by developers to ensure the model's integrity and accuracy, and determining its suitability. You will design evaluations to test the model's predictive power and robustness under uncertainty, develop alternative benchmark models, and ensure compliance with regulatory standards. Collaboration and proactive engagement with seniors, AMs, and Managers will be essential to meet key project deliverables. You will take responsibility for key deliverables, engage with Partners/Directors to comprehend project scope and business requirements, and coordinate with onshore and offshore teams for successful delivery. Additionally, you will advise non-audit clients on the implications of evolving provision accounting standards (IFRS9) and assist them in validating or developing credit risk measurement models. Qualifications required for this role include an advanced degree in Mathematics, Statistics, Economics, or other analytical disciplines. A graduate degree plus an MBA in Finance, coupled with relevant experience or exposure, is also considered. Additional certifications such as FRM or CFA are preferred. Preferred candidates will have 2-12 years of experience in Risk Management/Analytics at major banks, top tier consulting firms like Big 4, or captives of well-known banks. Proficiency in programming languages such as Python, SAS, and R is required. Advanced skills in statistical and quantitative modeling, including linear regression, logistic regression, ARIMA, Markov Chain, Merton Model, CHAID, and other predictive modeling techniques are essential. In-depth knowledge of regulatory requirements pertaining to model risk management, such as SR11-7, SR15-18, PRA, and EBA guidelines, is also expected.,