Job Responsibilities
Data Product Owner & Predictive Modeling
- Lead Predictive Analytics Roadmaps: Partner with the Director to prioritize and manage data products, including lead scoring, propensity models, next-best-action, upsell/cross-sell engines, and Customer Lifetime Value (LTV) models.
- Industrialize Analytics: Establish scalable processes, automation frameworks, and model deployment standards across various business units.
- Model Governance: Collaborate with Data Science teams to ensure predictive models are explainable, operationally scalable, and consistently monitored for performance, retraining, and fairness checks.
Customer Strategy & Growth Insights
- Lifecycle Optimization: Conduct deep-dive analyses to uncover insights that drive customer acquisition, engagement, and long-term retention.
- Advanced Segmentation: Build and refine advanced customer segmentation frameworks to support targeted multi-channel marketing and highly personalized policyholder communications.
- Customer Journey Analytics: Work with Customer Experience (CX) and Digital product teams to map digital footprints (e.g., app, e-commerce, customer portal), quantify drop-off touchpoints, and resolve repeat contact drivers.
Campaign Measurement & Experimentation
- Own Campaign Measurement: Design evaluation frameworks for digital and CRM marketing campaigns using attribution modeling, incrementality testing, and Marketing Mix Modeling (MMM) to optimize budget allocation.
- Run A/B Testing: Design and execute structured experimentation libraries (e.g., A/B testing, multi-armed bandits) across web platforms, applications, push notifications, and call center scripts.
Cross-Functional Stakeholder Management
- Act as the Core SME: Serve as the Subject Matter Expert (SME) and primary bridge connecting Marketing, Product Development, and Business Units with Data Science, Data Engineering, and IT teams.
- Translate Data for Executives: Translate technical metrics and complex data findings into practical, executive-ready presentations and dashboards (using Power BI or Tableau) to influence senior leadership decisions.
Data Infrastructure & AI Transformation
- Standardize Customer Data Foundations: Partner with Data Engineering to build robust data pipelines (e.g., via Spark or Databricks) ensuring that transactional, behavioral, and research data are integrated, secure, and standardized.
- Drive AI & Automation: Support the implementation of GenAI and automation initiatives that elevate operational efficiency and automate recurring "signal" reports.
- Data Governance & Privacy: Ensure all deployed customer analytics products comply strictly with regional data privacy laws, governance guidelines, and insurance compliance standards
Job Requirements
- Bachelor's degree holder
- A minimum of 12 years of relevant experience gained from large insurance firms
- Fluent Chinese and English
