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.
