Job Description

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 business and functional teams to understand modelling intent, design rationale, and underlying assumptions.

• Contribute to exploratory AI/ML proof‑of‑concept (POC) initiatives to deepen technical understanding, enhance validation methods, and support innovation within DMO.

Requirements

• Preferably a postgraduate degree in Data Science, Statistics, Mathematics, Analytics, Computer Science, or quantitative discipline.

• At least 4 years of hands‑on experience in model development, model validation, quantitative analytics, or AI/ML evaluation within financial institutions or similarly regulated environments.

• Strong theoretical and practical knowledge of machine learning, AI, statistical models, and model validation techniques.

• Strong understanding of feature engineering, feature selection, and data quality checks.

• Proficiency in evaluating model performance and diagnostics across statistical, ML, and AI models.

• Understanding of explainability techniques, including SHAP, LIME, and other model interpretation methods.

• Analytical skills to identify modelling weaknesses, design flaws, and performance gaps.

• Strong reporting skills to produce high-quality validation deliverables.

• Familiarity with Responsible AI concepts such as fairness, transparency, and robustness.