Deputy Manager of Data Analytics

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).
Collaborate with departments to standardize reporting requirements and automate information flows.
Integrate data from multiple sources (MB Life core system, CRM, Apps, etc.) to ensure accuracy and consistency.

2, Business Data Analysis

Translate business questions into analytical problems and build end- to- end analytical frameworks.
Extract and analyze customer behavior and distribution channel data to optimize retention, cross- selling, reactivation, and improve CLV.
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

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.
Establish processes to ensure efficient use and analysis of data across the company.

1, Education & Knowledge: Bachelor’s degree or higher in Data Science, Mathematics & Informatics, IT, Statistics, or related fields.
2, Experience:

Experience in insurance, banking, or finance is preferred.
At least 5 years of working experience, including 3 years in a managerial role in Data Analytics/BI/AI teams.

3, Technical Skills:

Solid knowledge of data architecture (data mart, ETL), customer data, and digital behavior analytics.
Proficiency in SQL, Python, and BI tools (Power BI/Tableau).
Hands- on experience in building reporting systems and deploying AI/ML models.

4, Soft Skills: Strong communication and presentation skills, effective with both technical and executive audiences.

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Cập nhật gần nhất lúc: 2025-10-02 03:20:02