Lead, Health Data & Preferred Provider Management

Prudential plc; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Prudential plc is seeking a health analytics leader to define and execute the analytics strategy for health provider networks. You will work with data science and data engineering teams to deliver dashboards, data models, and scalable analytics while aligning with business goals.

The role requires translating complex problems into actionable data solutions, engaging senior stakeholders, and staying ahead of trends in health analytics to improve outcomes and customer experience.

Qualifications

• Degree in data analytics, data science or related field.
• 5+ years in data analytics/data science in insurance/health.
• Familiarity with end-to-end analytics solutions and best practices.

Responsibilities

• Define and execute the health analytics strategy and roadmap.
• Lead end-to-end delivery of health provider analytics and dashboards.
• Collaborate with data science/engineering to build data models and pipelines.
• Translate stakeholder needs into data-driven solutions and insights.
• Present findings to senior management with clear visuals.

Skills

Python

SQL

Spark

Data analytics

Communication

Education

Degree in data analytics / data science / statistics / mathematics / computer science

Tools

Azure

Databricks

MLflow

Job description

In this role, you will support the objectives of Prudential's health strategy, develop a roadmap and use cases that align with it, and lead the delivery of preferred provider health data analytics. You will work closely with data science and data engineering teams to leverage data and insights to improve health outcomes and customer experience. You will also collaborate with business stakeholders and external partners, to understand their needs and provide actionable recommendations.

Job Description:

• Define and execute the health preferred provider analytics strategy and roadmap, ensuring alignment with Prudential's health vision and goals

• Manage the end-to-end delivery of health provider analytics, from dashboard ideation to data interpretation, negotiation with preferred providers, deployment of feedback mechanism for preferred providers and ensuring quality, timeliness, and scalability

• Work with data science and data engineering teams to design, develop and test data models, algorithms, and pipelines that support health provider network analytics use cases

• Work with business stakeholders and external partners to understand their requirements, challenges, and opportunities, and provide data-driven solutions and insights

• Communicate and present health network analytics findings and recommendations to senior management and relevant audiences, using clear and compelling visualizations and narratives

• Stay abreast of the latest trends, technologies, and best practices in health analytics, and identify opportunities for innovation and improvement

• Support the Group Health Claims recovery team in identifying leads, monitoring recoveries, setting recovery targets

• Ability to work independently and in a team, and to manage multiple tasks and deadlines

Who we are looking for:

Competencies & Personal Traits

• Strong communication and presentation abilities to convey complex technical concepts to non-technical stakeholders

• Enthusiastic about improving health outcomes and customer experience through data and analytics

Education & Working Experience

• Degree in data analytics, data science, statistics, mathematics, computer science, or related field

• At least 5 years of experience in data analytics, data science, or related role in the insurance or health industry

• Familiarity with end-to-end analytics solutions build and servicing, code writing, and code testing best practices is essential

• Experience in translating business problems into data and analytics solutions and influencing senior stakeholders and business partners in their adoption

• Proficiency in programming languages and tools (e.g., Python, SQL, Spark)

• Understanding of cloud-based data platform and analytics technologies (e.g., Azure, Databricks, MLflow)