Model Risk Analyst

Job Description Job Title: Model Risk Analyst Job Location: Hyderabad Experience: Up to 5 Years Relevant Experience: 12 Years in Model Risk Management, Model Validation, AI/ML Model Evaluation, Statistical Analysis, AI Governance, or related technologies Job Description We are looking for a talented Model Risk Analyst to design, implement, and support independent validation and risk assessment of Artificial Intelligence (AI) and Machine Learning (ML) models across the enterprise. The ideal candidate will work closely with business, data science, engineering, and governance teams to evaluate model performance, identify model risks, validate assumptions, ensure regulatory compliance, and strengthen enterprise AI governance practices. Key Responsibilities Perform independent validation and risk assessment of AI and Machine Learning models throughout the model lifecycle. Evaluate model design, methodology, assumptions, data quality, feature engineering, and model performance. Assess model accuracy, robustness, stability, fairness, explainability, and reliability using statistical techniques. Identify model risks, limitations, biases, and governance gaps, and recommend mitigation strategies. Validate model documentation, development standards, testing evidence, and governance artifacts. Review model implementation, monitoring processes, and performance metrics to ensure ongoing model effectiveness. Collaborate with Data Scientists, ML Engineers, Risk Management, Compliance, Internal Audit, and Business teams. Support model governance activities including model inventory, validation reports, approvals, and audit readiness. Develop validation reports, risk assessments, technical documentation, and governance evidence. Ensure compliance with enterprise AI governance policies, model risk management standards, and regulatory requirements. Contribute to continuous improvement of model validation methodologies, governance frameworks, and best practices. Required Skills Strong understanding of Model Risk Management (MRM) and Model Validation methodologies. Experience evaluating AI and Machine Learning models using statistical and analytical techniques. Knowledge of supervised and unsupervised machine learning algorithms, model evaluation metrics, and validation techniques. Experience performing statistical analysis, hypothesis testing, model performance assessment, and data quality validation. Familiarity with AI governance, model lifecycle management, and risk management frameworks. Understanding of model explainability, fairness, bias detection, drift monitoring, and model stability. Experience preparing technical validation reports, governance documentation, and audit evidence. Hands-on experience with Python, SQL, R, or statistical analysis tools. Strong analytical thinking, documentation, and problem-solving skills. Excellent communication and stakeholder management abilities. Preferred Qualifications Experience with Model Risk Management frameworks such as SR 11-7, NIST AI RMF, ISO/IEC 42001, or similar governance standards. Knowledge of Responsible AI principles, AI Governance, Explainable AI (XAI), and AI Risk Management. Experience using model validation and explainability tools such as SHAP, LIME, Fairlearn, IBM AI Fairness 360 (AIF360), or Microsoft Responsible AI Toolbox. Familiarity with MLOps platforms, model monitoring, model registries, and AI lifecycle management. Experience with cloud AI platforms such as Azure Machine Learning, AWS SageMaker, Google Vertex AI, or Databricks. Understanding of regulatory compliance, data privacy, AI auditing, and governance requirements. Relevant certifications in AI Governance, Risk Management, Data Science, Statistics, or Machine Learning are an added advantage. Candidate Details Total Experience: Relevant Experience: Current Company: Current CTC: Expected CTC: Notice Period: Current Location: Reason for Change: Please send your updated profile to [Confidential Information] .