<h2 style="text-align:justify;margin:10.0pt 0.0in 0.0in;line-height:115%;font-size:13.0pt;font-family:Calibri, sans-serif;color:#4f81bd">Responsibilities</h2> • 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. <h2 style="text-align:justify;margin:10.0pt 0.0in 0.0in;line-height:115%;font-size:13.0pt;font-family:Calibri, sans-serif;color:#4f81bd"> </h2> <h2 style="text-align:justify;margin:10.0pt 0.0in 0.0in;line-height:115%;font-size:13.0pt;font-family:Calibri, sans-serif;color:#4f81bd">Requirements</h2> • 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.

AVP/VP, Data Scientist, Data Management Office (Singapore) @ Sumitomo Mitsui Banking Corporation

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