Description
Purpose
Build and lead Chubb Life's Analytics function globally, covering data science, predictive modelling, scoring, cognitive use case design, and business intelligence across 8 Asian markets.
Responsibilities
Data Science, Scoring and Predictive Modelling
- Develop production-grade ML and statistical models across core insurance domains: agent scoring, persistency and lapse prediction, customer lifetime value, cross-sell propensity, risk segmentation, and claims analytics.
- Define modelling methodology, feature engineering standards, and model validation frameworks used globally.
- Own the full model development lifecycle from hypothesis through training, validation, and structured handoff to AI Engineering for deployment.
Cognitive Analytics and GenAI Use Case Design
- Own the intelligence design layer for GenAI and cognitive AI applications - defining what the AI should know, reason about, and decide, with measurable success criteria.
- Partner with the Head of AI Engineering to translate cognitive use cases into well-specified engineering requirements.
- Continuously evaluate emerging ML/AI techniques and identify which are ready for production adoption in an insurance context.
Business Intelligence and Descriptive Analytics
- Govern the BI and descriptive analytics layer - ensuring business leaders have timely, trusted insights through PowerBI and Insights Studio.
- Define the analytics taxonomy: KPIs, metric definitions, and reporting standards across all business functions.
- Build self-serve analytics capability within a governed, scalable framework.
Cross-Functional Analytics
- Deliver analytics value across Distribution/Agency, Actuarial/Risk, Customer, and Operations.
- Translate business questions into scoped analytical problems with clear hypotheses and commercial success criteria.
- Ensure every model and insight connects directly to a measurable business outcome.
Team Building and Capability Development
- Build the Analytics function from scratch - define team structure, hiring profiles, and operating model for data scientists and ML practitioners.
- Foster a culture that balances academic depth with delivery velocity: research thinking and production discipline, coexisting.
- Mentor team members in statistical modelling, ML engineering, and applied AI.
Governance and Standards
- Define modelling conventions, peer review protocols, documentation standards, and model risk management practices.
- Ensure all models meet responsible AI principles: fairness, explainability, regulatory compliance, and data privacy.
- Maintain model inventories and governance documentation for audit and regulatory purposes.
Qualifications
- 12-15+ years in data science, advanced analytics, or quantitative research; 4+ years leading analytical teams.
- Insurance industry experience is critical.
- Deep expertise in statistical modelling and ML: supervised/unsupervised learning, ensemble methods, survival models, NLP, and deep learning.
- Production track record: models built, validated, and deployed at scale in insurance or financial services.
- Experience designing the analytical intelligence layer for GenAI/cognitive AI applications.
- Strong command of Python, scikit-learn, XGBoost, PyTorch, Databricks MLflow, and Snowflake.
- Proven ability to build analytics teams from scratch and develop talent pipelines.
- Communicates complex quantitative findings to non-technical executives with clarity and confidence.
- PhD or Master's in Statistics, Mathematics, Computer Science, Actuarial Science, or a related quantitative discipline strongly preferred.