Lead, Health Data & Preferred Provider Management

About the role

This role supports Prudential's health strategy by developing a roadmap and use cases aligned with it, and leading the delivery of preferred provider health data analytics. You will work closely with data science and data engineering teams to turn data and insights into better health outcomes and customer experience, and collaborate with business stakeholders and external partners on their needs.

Key responsibilities

• Define and execute the health preferred provider analytics strategy and roadmap, aligned with Prudential's health vision and goals.
• Manage end-to-end delivery of health provider analytics, from dashboard ideation to data interpretation, including negotiating with preferred providers, deploying feedback mechanisms and ensuring quality, timeliness and scalability.
• Work with data science and data engineering teams to design, develop and test data models, algorithms and pipelines supporting health provider network analytics.
• Work with business stakeholders and external partners to understand their requirements and provide data-driven solutions and insights.
• Present analytics findings and recommendations to senior management with clear visualizations and narratives.
• Stay up to date with trends and best practices in health analytics and identify innovation opportunities.
• Support the Group Health Claims recovery team in identifying leads, monitoring recoveries and setting recovery targets.

Required profile

• Degree in data analytics, data science, statistics, mathematics, computer science or a related field.
• At least 5 years of experience in data analytics, data science or a related role in the insurance or health industry.
• Familiarity with building and servicing end-to-end analytics solutions, and coding best practices.
• Experience turning business problems into data solutions and influencing senior stakeholders and partners to adopt them.
• Ability to work independently and in a team, managing multiple tasks and deadlines.

Required skills

• Programming proficiency, for example Python and SQL.
• Cloud-based data platforms and analytics technologies such as Azure, Databricks and MLflow.
• Strong communication and presentation skills to explain complex technical concepts to non-technical audiences.