VP, AI & Emerging Analytics

Reinsurance Group of America•Chesterfield, MO

37d

About The Position

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.

Requirements

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.

Nice To Haves

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., TensorFlow, 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 LangChain, LangGraph, DSPy, or LlamaIndex for building complex AI workflows, multi-step reasoning chains, and agentic systems that combine LLMs with tools, memory, and structured decision-making processes.
Understanding of DevOps and MLOps practices for model deployment and monitoring.
Knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes).
Experience with graph databases and network analysis techniques.
Familiarity with natural language processing (NLP) and computer vision algorithms and applications.
Understanding of time series analysis and forecasting methods relevant to the insurance industry.
Knowledge of actuarial concepts including mortality, morbidity, and persistency studies.
Knowledge of life, health, and/or annuity products.
Knowledge of life insurance underwriting and biometric risk analysis.
Knowledge of insurance risk analysis.
Experience in computational finance, econometrics, statistics and math.

Responsibilities

Strategic Leadership
Continue to develop, support, and execute the region's long-term strategy for AI, machine learning, and data science initiatives.
Align data science projects with overall business objectives, key performance indicators, and growth drivers.
Collaborate with regional leads and executives to identify opportunities for data-driven innovation and growth.
Team Management and Development
Lead, mentor, and inspire a team of data scientists, machine learning engineers, and data engineers.
Foster a culture of innovation, continuous learning, and technical excellence.
Develop and support career progression paths for team members.
Project Oversight and Delivery
Oversee the end-to-end lifecycle of multiple concurrent data science projects.
Ensure timely delivery of high-quality, scalable solutions that meet business requirements.
Manage resource allocation and prioritize projects based on business impact and strategic importance.
Technical Leadership and Innovation
Stay at the forefront of AI, machine learning, and statistical modeling advancements.
Evaluate and recommend new technologies, methodologies, and tools to enhance the team's capabilities.
Provide technical guidance and expertise on complex data science problems.
Stakeholder Management and Communication
Present project outcomes, insights, and recommendations to regional leadership.
Collaborate with business units to identify opportunities for applying data science solutions.
Build and maintain relationships with external partners, and vendors.
Governance and Compliance
Ensure all data science initiatives adhere to regulatory requirements and ethical AI principles.
Develop and enforce best practices for data governance, model validation, and deployment.
Collaborate with legal and compliance teams to address data privacy and security concerns.
Financial Management
Develop and manage the annual budget for the Emerging Analytics team.
Analyze and report on the ROI of data science initiatives.
Make strategic decisions on technology investments and resource allocation to support team's initiatives.
Technology and Infrastructure
Continue to advance RGA's data science and AI technology and supporting infrastructure by partnering with Global Technology.
Identify opportunities for common solution architectures and patterns to drive scalability in the business and strengthen execution efficiency.
Oversee MLOps best practices for the team and drive adoption and adherence to enterprise standards and innovative technology.

Benefits

Gain valuable knowledge from and experience with diverse, caring colleagues around the world.
Enjoy a respectful, welcoming environment that fosters individuality and encourages pioneering thought.
Join the bright and creative minds of RGA, and experience vast, endless career potential.

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