Senior Model Risk Analyst - Mumbai

Job Description: About the Role The Senior Model Risk Analyst in the Market Risk function under the Risk business unit is responsible for assessing and validating quantitative models used across Treasury, Wholesale, and Retail functions. These models, which support internal decision-making and product disbursement, are governed by the banks Model Risk Management framework. The role involves evaluating model performance, quantifying model risk, and ensuring alignment with industry best practices. The incumbent will also contribute to enhancing the model risk governance framework and communicating risk insights to senior management. Key Responsibilities Primary Responsibilities Conduct model validation using techniques such as econometrics, financial engineering, advanced statistics, machine learning, and data analysis. Evaluate and validate models across domains including Retail Analytics, Trading Risk, and Wholesale Credit Risk. Quantify model risk and prepare periodic reports to communicate the banks model risk status to senior management. Review and enhance the banks Model Risk Management framework in line with industry best practices. Maintain the banks model register and associated validation documentation to support the model risk governance process. Communicate effectively with senior management and business heads through clear and concise written and verbal updates. Secondary Responsibilities Recommend improvements to existing models to enhance business efficiency and decision-making. Stay updated with the latest trends in model development, maintenance, and risk management. Education Graduation: Bachelors degree in a quantitative discipline such as Engineering, Mathematics, Statistics, Engineering, Economics, or related fields Post-graduation: Masters degree or MBA in Finance (preferred) Experience Minimum of 2 years of relevant experience in model validation, development, or risk management, particularly in Retail, Wholesale, or Market Risk domains Skills and Attributes Strong expertise in quantitative modeling techniques and statistical tools Proficiency in programming languages such as Python, R, or SAS Deep understanding of model risk governance and regulatory expectations Excellent analytical, problem-solving, and documentation skills Strong communication skills for engaging with senior stakeholders Ability to manage multiple validation projects independently .