VP, AI & Emerging Analytics

Vice President Of Ai And Emerging Analytics

RGA is a purpose-driven organization working to solve today's challenges through innovation and collaboration. A Fortune 200 Company and listed among its World's Most Admired Companies, we're the only global reinsurance company to focus primarily on life- and health-related solutions. Join our multinational team of intelligent, motivated, and collaborative people, and help us make financial protection accessible to all.

The Vice President of AI and Emerging Analytics is a leadership role responsible for driving the strategic direction and execution of AI, machine learning, and advanced analytics initiatives across the region. This position leads a team of data scientists, machine learning engineers, and data engineers to develop and implement cutting‑edge, data‑driven solutions that enhance business operations, improve decision‑making processes, and create competitive advantages for RGA.

• Strategic Leadership
• Team Management and Development
• Project Oversight and Delivery
• Technical Leadership and Innovation
• Stakeholder Management and Communication
• Governance and Compliance
• Financial Management
• Technology and Infrastructure

• Bachelor's degree in Computer Science, Math, Statistics, Actuarial Science, Finance, Economics or related field.
• 15+ years of analytics experience or in developing statistical models for insurance or related applications.
• Proven track record of successfully leading large‑scale AI and machine learning initiatives in a Fortune 500 environment.
• Deep understanding of insurance industry dynamics and challenges, with 5+ years of experience in the sector.
• Strong background in statistical modeling, machine learning algorithms, and data engineering principles.
• Experience in managing and scaling data science teams.
• Exceptional leadership skills with the ability to inspire and motivate high‑performing technical teams.
• Strong strategic thinking and ability to translate business problems into data science solutions.
• Expert knowledge of modern data science tools, cloud platforms, and big data technologies.
• Excellent communication skills, able to explain complex technical concepts to both technical and non‑technical audiences.
• Strong product management skills with the ability to manage multiple complex initiatives simultaneously.
• Deep understanding of data governance, ethics, and regulatory compliance in the context of AI and machine learning.
• Strong understanding of large language models, transformer architectures, and modern generative AI systems, combined with the ability to evaluate model capabilities, limitations, and appropriate use cases for business applications.
• Ability to balance technical expertise with business acumen to drive value creation.
• Strong negotiation and conflict resolution skills.
• Adaptability and resilience in a fast‑paced, evolving technological landscape.
• Capacity to translate AI capabilities into real‑world products while navigating complex ethical considerations around safety, bias, privacy, and responsible deployment at scale.
• Highly Advanced ability to translate business needs and problems into viable / accepted solutions.
• Highly Advanced ability to liaise with individuals across a wide variety of operational, functional and technical disciplines.

• Master's degree or PhD in Statistics, Actuarial Science, Business, Finance, Economics, or related field.
• 10+ years of experience with statistical modeling for insurance (Decision Trees, Time Series, Regression, reinforcement learning, unsupervised learning algorithms, etc.).
• 5+ years of experience in working with and deploying generative AI technologies into an enterprise setting.
• Knowledge of reinsurance, life insurance, and financial markets.
• Proficiency in multiple programming languages such as Python, R, and SQL.
• Expertise in machine learning frameworks and libraries (e.g., Tensor Flow, PyTorch, scikit‑learn).
• Strong knowledge of cloud computing platforms (e.g., AWS, Azure, GCP) and their ML / AI services.
• Experience with big data technologies such as Snowflake, Databricks, Spark, and distributed computing.
• Proficiency with modern frameworks like…