validation of credit risk (e.g., underwriting and limit management), finance (provisioning, offloading, profitability), and other models, rigorously reviewing and challenging all aspects: conceptual soundness, data integrity, feature engineering and selection, training and testing, regulatory compliance and fairness, documentation, deployment, monitoring and business impact. Independently replicate the model development process … where necessary and conduct challenger analyses. Collaborate closely with first-line data scientists, machine learning (ML) engineers, and product stakeholders to understand models’ business context and ensure transparent communication of model risks and validation findings. Provide actionable recommendations and formally document validation outcomes in line with internal model governance ...