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

, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

08 Jul, 26

Salary

0.0

Posted On

09 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Model Validation, AI Evaluation, Statistical Modeling, Feature Engineering, SHAP, LIME, Model Explainability, Quantitative Analytics, Data Quality, Responsible AI, LLMs, Prompt Engineering, Sensitivity Analysis, Risk Management

Industry

Financial Services

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.

Responsibilities

The role involves independently assessing and validating AI/ML models, including LLMs and agentic workflows, to ensure logical soundness and performance. You will produce comprehensive validation reports and collaborate with cross-functional teams to uphold Responsible AI principles.