What are the responsibilities and job description for the VP, Lead Data Scientist, Generative AI position at RGA Reinsurance Company?

You desire impactful work.

You're RGA ready

RGA is a purpose-driven organization working to solve today's challenges through innovation and collaboration. A Fortune 500 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.

Position Overview

Americas Data Solutions is seeking an accomplished senior data science leader to drive RGA America's generative AI initiatives and oversee a team of data scientists developing cutting-edge AI solutions. This strategic role combines deep technical expertise in generative AI architectures with proven business acumen to translate innovative AI capabilities into tangible business value. The ideal candidate will orchestrate the complete AI solution lifecycle for key initiatives - from initial discovery and ideation through architecture design, development, deployment, and ongoing optimization - while establishing best practice frameworks and technical standards that ensure successful delivery of strategic AI initiatives at scale.

Responsibilities

Develop and execute generative AI initiatives across the region, including roadmap development, architecture design, resource allocation, value realization, and alignment with business objectives
Lead and mentor team of data scientists, establishing best practices for solution design, model development, and deployment while fostering a culture of innovation and technical excellence
Engage with business stakeholders to identify high-impact opportunities for generative AI applications, develop business cases, and drive adoption across the organization
Establish architectural standards, governance frameworks, and best practices for responsible AI development, ensuring scalability, security, and ethical considerations are addressed
Design and oversee iterative project plans that balance rapid experimentation with production-ready delivery, ensuring effective resource utilization, technical excellence, and risk management
Build and maintain strategic capabilities and architectural patterns with key technologies and leverage emerging technologies and services to accelerate solution development
Maintain a modern understanding of emerging technical and data science technologies, best practices, and architectures ensuring this knowledge is disseminated and applied to initiatives
Drive the development of reusable components, design patterns, and technical standards that enable efficient scaling of AI initiatives across business units

Requirements

Bachelor's degree in Computer Science, Math, Data Science, Machine Learning, or related technical field

10-15 years of machine learning experience

5 years of management experience

Extensive experience architecting and implementing enterprise-scale generative AI solutions, with deep knowledge of foundation models (GPT-4, Claude, PaLM), fine-tuning strategies, and prompt engineering techniques

Proven track record of leading and growing high-performing data science teams, with exceptional mentoring and coaching abilities

Expert knowledge of large language model deployment patterns, including RAG architectures, vector databases (Pinecone, Weaviate, ChromaDB), and embedding models

Advanced proficiency in Python and key ML frameworks including PyTorch, Transformers, LangChain, and LlamaIndex for production AI applications

Highly advanced statistical modelling, machine learning and other modelling skills including but not limited to the use of Generalized Linear Modelling, Cluster analysis techniques, Random Forest and gradient boosting methods.

Strong expertise in AWS services and architectural patterns for AI / ML workloads, including SageMaker, Bedrock, ECS / EKS, and serverless computing

Demonstrated experience with MLOps tools and practices including Weights & Biases, MLflow, DVC, and CI / CD pipelines for ML models

Deep understanding of model optimization techniques including quantization, distillation, and efficient inference strategies for production deployment

Experience with distributed training systems, GPU resource management, and scaling strategies for large model training

Advanced knowledge of data pipeline architectures using tools like Airflow, Prefect, dbt or Dagster for managing ML workflows

Expertise in AI governance frameworks, including model risk management, ethical AI principles, and regulatory compliance standards

Strong background in evaluation metrics and testing frameworks for generative AI systems, including automated evaluation pipelines

Proficiency with modern data storage solutions including data lakes (S3, Delta Lake), data warehouses (Snowflake, Databricks), and streaming architectures (Kafka, Kinesis)

Outstanding communication and presentation skills, with ability to effectively engage with technical and non-technical stakeholders at all levels

Strong stakeholder management skills with proven ability to build consensus across diverse groups

Experience with agile methodologies and iterative development approaches for AI projects, including sprint planning and resource allocation

Familiarity with containerization and orchestration technologies including Docker, Kubernetes, and service mesh architectures

Demonstrated ability to be hands-on technical leader who can architect solutions while actively coding alongside the team, including developing proof-of-concepts, debugging complex issues, and contributing to critical components of generative AI solutions

Expert ability to liaise with individuals across a wide variety of operational, functional and technical disciplines

Preferred

Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Computational Science or related technical field

Advanced certifications in cloud platforms or machine learning specializations

Knowledge of real-world data, medical, and underwriting data

Knowledge of actuarial processes and concepts

Advanced life insurance and / or reinsurance industry and product knowledge.

Knowledge of life insurance underwriting

LI-REMOTE #LI-SP2

What you can expect from RGA :

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.

Compensation Range :

146,950.00 - $218,950.00 Annual

Base pay varies depending on job-related knowledge, skills, experience and market location. In addition, RGA provides an annual bonus plan that includes all roles and some positions are eligible for participation in our long-term equity incentive plan. RGA also maintains a full range of health, retirement, and other employee benefits.

RGA is an equal opportunity employer. Qualified applicants will be considered without regard to race, color, age, gender identity or expression, sex, disability, veteran status, religion, national origin, or any other characteristic protected by applicable equal employment opportunity laws.

Salary : $146,950 - $218,950

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