Data Science Manager (Quản Lý Khoa Học Dữ Liệu)

Prudential's purpose is to be partners for every life and protectors for every future.

The Data Science Manager is expected to deliver consistent, high-quality outcomes within a defined area of data science and analytics, working independently most of the time.

Job Responsibilities

• Design, build, and maintain a scalable Customer Insight Feature Store that consolidates customer data from multiple sources to create a unified customer view, enabling analysis of customer behavior, profile enrichment, and generation of actionable insights.
• Develop, enhance, and own the delivery of advanced analytics and machine learning models, including CLV, customer retention, segmentation, upsell/cross-sell recommendation, and fraud detection.
• Support AI adoption and innovation initiatives by identifying practical AI use cases, advising on AI-enabled analytics solutions, and coordinating with business, technology, and governance stakeholders to ensure responsible experimentation and phased rollout.
• Apply and follow MLOps practices across the model lifecycle, ensuring model quality, deployment reliability, and ongoing monitoring of model performance, data drift, and model drift.
• Collaborate with local, regional, business, and technology stakeholders to align customer analytics initiatives with market needs, strategic priorities, and operational requirements.
• Stay up to date with industry trends, best practices, and emerging technologies in data science, AI, big data analytics, and customer insights; evaluate and recommend relevant tools and methodologies to enhance analytics capability.

Job Requirements

• Bachelor's degree in Computer Science, Information Technology Engineer, or equivalent fields.
• Master's degree in Computer Science, Financial analysis business fields (e.g. MBA, Finance, Business Analytics, etc.) would be considered as a strong plus.
• 5 years or experience working in Data Scientist, with a focus on customer behavior data and recommendation models.
• Strong technical understanding of machine learning, statistic, and data mining.
• Familiarity with MLOps and DevOps practices for managing data pipelines and model deployment.
• Strong programming skills (e.g., Python, SQL, ...).
• Excellent communication and interpersonal skills, with the ability to collaborate effectively across teams and regions.