JOB PURPOSE: Responsible for building a centralized reporting system, leveraging data for in-depth analysis, and developing data and machine learning models to support business activities (e.g., customer segmentation, behavior prediction). The role also involves deploying AI applications to enhance company-wide operational efficiency and decision-making.
KEY RESPONSIBILITIES:
1, Design & Implement Centralized Reporting System
- Develop and maintain management reporting systems and multi-level KPI dashboards (ExCo, departments, partners).
- Integrate data from multiple sources (MB Life core system, CRM, Apps, etc.) to ensure accuracy and consistency.
- Collaborate with departments to standardize reporting requirements and automate information flows.
2, Business Data Analysis
- Extract and analyze customer behavior and distribution channel data to optimize retention, cross-selling, reactivation, and improve CLV.
- Translate business questions into analytical problems and build end-to-end analytical frameworks.
- Execute or supervise segmentation models, lapse prediction, renewal behavior, upsell, and cross-sell models.
3, AI and Advanced Analytics Applications
- Lead implementation of AI/ML products: scoring, GenAI, fraud detection, automated underwriting, etc.
- Combine structured and unstructured data (digital footprints) to generate insights and behavioral models.
4, Process Development & Talent Building
- Establish processes to ensure efficient use and analysis of data across the company.
- Train teams of Data Analysts, AI Ops, and GenAI specialists under MBAL’s competency framework.
- Work closely with the Data Platform and Data Governance teams to ensure compliance with quality and security standards.
1, Education & Knowledge: Bachelor’s degree or higher in Data Science, Mathematics & Informatics, IT, Statistics, or related fields.
2, Experience:
- At least 5 years of working experience, including 3 years in a managerial role in Data Analytics/BI/AI teams.
- Experience in insurance, banking, or finance is preferred.
3, Technical Skills:
- Proficiency in SQL, Python, and BI tools (Power BI/Tableau).
- Hands-on experience in building reporting systems and deploying AI/ML models.
- Solid knowledge of data architecture (data mart, ETL), customer data, and digital behavior analytics.
4, Soft Skills: Strong communication and presentation skills, effective with both technical and executive audiences.
