AVP/VP, Data Scientist, Data Management Office - SMBC

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.