AM- M&V - UK - San

As a Model Validation/Audit/Review Specialist at KGS, your primary responsibility will be to conduct validation, audit, and review of credit loss forecasting models within the retail or wholesale domain, specifically focusing on IFRS9/IRB models and their PD/EAD/LGD components. In this role, you will need to understand relevant regulatory requirements, review development documentation, perform testing and benchmarking, create challenger models using SAS, R, or Python, and prepare comprehensive reports. Your duties will also include providing support for other model validation activities related to Underwriting scorecards, Credit Scoring, behavioral models, economic scenario models, and validation-related automation tasks as required. You will be responsible for assessing the conceptual soundness of models, critically evaluating testing conducted by developers, ensuring model integrity and accuracy, evaluating predictive power and robustness, and ensuring compliance with regulatory standards. Collaboration with seniors, AMs, and Managers will be essential to meet key project deliverables. You will be expected to take ownership of key deliverables, engage with Partners/Directors to understand project scope and business requirements, and coordinate with onshore and offshore teams for successful project delivery. Additionally, you will provide advice to non-audit clients on the implications of evolving provision accounting standards (IFRS9) and help them validate or develop credit risk measurement models. Qualifications for this role at KGS include an advanced degree in Mathematics, Statistics, Economics, or other analytical disciplines. Alternatively, a graduate degree along with an MBA in Finance and relevant experience or exposure will be considered. Additional certifications such as FRM or CFA are preferred. Ideal candidates should 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 various techniques like linear regression, logistic regression, ARIMA, Markov Chain, Merton Model, CHAID, and other predictive modeling methods, are essential. In-depth knowledge of regulatory requirements related to model risk management, such as SR11-7, SR15-18, PRA, and EBA guidelines, is also expected.,