About this role
The Model Validation Analyst is responsible for independently assessing the conceptual soundness, performance, implementation, and ongoing monitoring of models used across the Bank. The role supports the Bank's Model Risk Management (MRM) framework by conducting rigorous validations in accordance with regulatory requirements, including the U.S. Federal Reserve's SR 26-2 Guidance on Model Risk Management and applicable OCC and FDIC expectations. The analyst will evaluate a variety of quantitative and qualitative models, including credit risk, market risk, liquidity risk, stress testing, AML/fraud, capital planning, CECL, pricing, and machine learning models. The successful candidate will work under the supervision of the U.S. team, work closely with bank MRM team, model developers, model owners, business stakeholders, Internal Audit, and regulators to ensure models are fit for purpose and risks are appropriately identified and mitigated. Perform independent validations and reviews of models/EUCs across the model lifecycle under supervision of the US team. Assess model conceptual design, methodology, assumptions, limitations, data quality, implementation, and usage. Conduct quantitative testing, including: Outcome analysis Benchmarking Back-testing Sensitivity analysis Stress testing Performance monitoring reviews Evaluate model development documentation and supporting evidence. Verify compliance with internal model risk policies and regulatory guidance. Assign validation findings and risk ratings based on established governance standards. Develop recommendations for model improvements and risk mitigation. Assist in maintaining the model inventory, model risk ratings, and issue tracking and remediation. Collaborate with bank MRM team, model developers, business units, risk management teams, and technology teams. Education Bachelor's degree in Statistics, Mathematics, Economics, Finance, Data Science, or related quantitative discipline Experience 2-5 years of experience in model validation, model development, quantitative analytics, data science, risk management, or related fields within banking or financial services, preferably for U.S. clients. Familiarity with model risk management frameworks and regulatory expectations. Experience validating or developing risk, financial, or machine learning models is preferred. Technical Skills Strong understanding of statistical and quantitative modeling techniques. Knowledge of: Regression analysis Time series forecasting Classification models Machine learning methodologies Probability and statistical inference Proficiency in one or more programming languages: Python R SAS SQL Experience working with large datasets and analytical tools. Master's degree in a quantitative field. Experience validating: Credit risk models CECL models Stress testing models Market risk models AML/Fraud models Machine learning and AI models · Experience or familiarity with: o SR 26-2 Model Risk Management Guidance o OCC Model Risk Management expectations o CECL framework o Basel regulations o Fair Lending and model governance requirements o AI/ML model risk considerations Competencies Strong analytical and critical thinking skills. Ability to independently challenge complex quantitative methodologies. Excellent written and verbal communication skills. Strong report-writing and documentation capabilities. Attention to detail and commitment to quality. Ability to manage multiple projects and deadlines. Strong stakeholder engagement and relationship management skills. Key Deliverables Comprehensive model validation reports. Validation test workpapers and supporting documentation. Findings and remediation tracking. Model performance monitoring assessments. India Enterprise Operations Professional Bangalore, IN (0063) IBM India Private Limited