Manager, Actuarial Data, Automation and AI

About the role

Prudential is looking for a forward-thinking actuarial manager to shape the future of reporting, analytics and AI within its Actuarial function. The Manager, Actuarial Data, Automation and AI (ADAA) drives initiatives that modernize reporting, unlock better business insights and scale practical AI solutions. The role reports to the Head of Actuarial Data, Automation and AI and works with Actuarial, Finance, Digital & Technology to improve reporting efficiency, strengthen controls and deliver actionable insights.

Key responsibilities

• Lead the implementation of actuarial initiatives within the Future Finance Program+, transforming processes through workflow automation, process redesign, enhanced reporting analytics and AI adoption.
• Identify business use cases and consolidate requirements with actuarial teams, and support solutioning, option analysis and test approaches.
• Act as PACS Project Manager, delivering PACS FFP+ initiatives and Actuarial Tech projects: define scope, timelines, budgets and resources, run risk analysis and contingency planning, and lead cross-functional teams.
• Assist in defining an actuarial AI roadmap, identify AI use cases for reporting and management information, and coordinate pilots and scaled AI solutions.

Required profile

• 6+ years in actuarial reporting, business intelligence/data analytics or business analyst roles.
• Project management or consulting experience in the insurance industry.
• AI solution implementation or data analytics experience is a plus.
• Qualified or near-qualified actuary, or an experienced actuarial professional with strong business credibility.
• Degree in Actuarial, Mathematics, Statistics, Business Analytics, Data Science or Computer Science.

Required skills

• Knowledge of actuarial reporting processes and ability to modernize them through automation.
• Design and implement end-to-end process flows with data validation, governance and quality standards.
• Actuarial analytics and ability to turn complex analysis into management insights.
• Proficiency in statistical models, Python, SQL and business intelligence tools (e.g. Power BI).
• Agile or Scrum methodology; machine learning or AI knowledge is an advantage.