Responsibilities

• Independently assess AI/ML/data science model purpose, assumptions, features, data inputs, and logical soundness.

• Evaluate feature engineering, data quality, and detect issues such as leakage or mis-specified inputs.

• Evaluate model performance using suitable metrics, diagnostic tests, and validation methodologies.

• Assess stability, robustness, sensitivity analysis, susceptibility to adversarial attacks and model or concept drift.

• Apply model explainability methods such as SHAP, LIME and other interpretability techniques.

• Produce comprehensive, well-reasoned Model Validation Reports.

• Evaluate AI/ML models, LLMs, retrieval-augmented systems, agentic workflows, and prompt-engineering methods.

• Ensure validation standards align with Responsible AI principles including fairness, transparency, and robustness.

• Collaborate with data scientists and model developers across ...