AVP/VP, Data Scientist, Data Management Office

The Japan Research Institute, Limited (Singapore Branch); Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

The Japan Research Institute, Limited (Singapore Branch) seeks an AVP/VP-level Data Scientist to lead model validation within the Data Management Office. You will assess AI/ML model purposes, data inputs, and assumptions, and validate feature engineering, data quality, and performance across regulated environments.

Responsibilities include developing comprehensive validation reports, evaluating model stability and robustness, applying SHAP/LIME explanations, and ensuring alignment with

Qualifications

• Postgraduate degree or equivalent in data science, statistics, analytics or related fields.
• Minimum 4 years hands-on model development, validation, or AI evaluation in regulated environments.
• Strong knowledge of ML/AI theory, model validation techniques, and data quality checks.
• Experience with feature engineering, model diagnostics and performance evaluation.
• Familiarity with responsible AI concepts like fairness and transparency.

Responsibilities

• Independently assess AI/ML model purpose, data inputs, and assumptions.
• Evaluate feature engineering and data quality; detect leakage or mis-specification.
• Assess model performance using appropriate metrics and validation methods.
• Assess stability, robustness, drift, and adversarial vulnerability of models.
• Apply explainability methods such as SHAP and LIME to interpret models.
• Produce comprehensive Model Validation Reports with clear recommendations.
• Evaluate AI/LLM retrieval-augmented systems and prompt-engineering methods.
• Ensure validation aligns with Responsible AI principles (fairness, transparency, robustness).
• Collaborate with data scientists and developers to understand intent and assumptions.
• Contribute to exploratory POC initiatives to deepen validation methods.

Skills

Model validation

Responsible AI

SHAP/LIME explainability

Data quality checks

Feature engineering

Statistical modelling

Education

Postgraduate degree in Data Science

Tools

Python

R

ML frameworks

Job description

AVP/VP, Data Scientist, Data Management Office

Date: Oct 1, 2026

Location:

Singapore

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.