# Head of Analytics, Chubb Life

**Company**: Chubb Life
**Location**: Hong Kong
**Experience**: executive
**Job type**: full-time
**Category**: Data Science
**Industry**: Insurance

**Apply**: https://fa-ewgu-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_2001/job/38145?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_7052d694-feb

## 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.

## Skills

### Required
- Python
- scikit-learn
- XGBoost
- PyTorch
- Databricks MLflow
- Snowflake
- statistical modelling
- ML engineering
- applied AI
- GenAI
- cognitive AI

### Nice to have
- PhD or Master's in Statistics, Mathematics, Computer Science, Actuarial Science

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