Senior Data Scientist at RGA Reinsurance Company in Chesterfield, MO

Senior Data Scientist Location: United States, Chesterfield, MO, RGA HQ You desire impactful work. You’reRGA ready 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. Position Overview The Senior Data Scientist at RGA plays a pivotal role in pioneering advanced machine learning (ML) and generative AI (GenAI) solutions that drive innovation in the insurance and reinsurance industry. Leveraging deep technical expertise, this leader independently architects and implements sophisticated analytical models to solve high-impact business challenges, powering RGA’s data-driven transformation. By collaborating closely with business stakeholders, the Senior Data Scientist translates complex risk and market insights into actionable solutions, mentors emerging talent, and ensures RGA remains at the forefront of predictive analytics and competitive advantage. Responsibilities End-to-End Modeling: Design, develop, and deploy sophisticated machine learning models that address mission-critical business challenges, including underwriting automation, pricing optimization, and claims analytics. This includes collaborating with business stakeholders to define requirements, selecting appropriate algorithms, engineering features, tuning model parameters, and integrating solutions into production environments for seamless business adoption. GenAI Solution Development: Lead the end-to-end development and implementation of generative AI solutions, leveraging large language models (LLMs) for advanced document processing, automated content creation, and streamlining repetitive business processes. Responsibilities include identifying high-value GenAI use cases, fine-tuning models for domain-specific tasks, and ensuring responsible AI practices such as bias mitigation and transparency. Technical Leadership: Serve as a technical authority and mentor for colleagues, providing expert guidance on best practices in machine learning modeling, code development, and solution architecture. This involves conducting code reviews, sharing knowledge of emerging technologies, and fostering a culture of technical excellence within the data science team. Project Leadership: Lead and manage small-scale projects, including defining scope and objectives, developing project plans, allocating resources, and coordinating activities across cross-functional teams. Maintain proactive communication with stakeholders to track progress, address risks, and ensure timely and successful project delivery aligned with business goals. Data Pipeline Architecture: Architect, develop, and maintain robust, automated data pipelines and ETL processes in partnership with data engineering teams. This includes designing scalable workflows for data ingestion, transformation, and validation, ensuring data quality and availability for analytics and modeling, and optimizing pipeline efficiency for large, complex datasets. Stakeholder Communication: Effectively communicate complex analytical findings, model insights, and actionable recommendations to a wide range of stakeholders—including business leaders and senior management—using clear visualizations and storytelling. Facilitate data-driven decision-making by translating technical results into business value and strategic impact. Model Governance: Champion and enforce rigorous model governance practices by conducting thorough model validation, ongoing monitoring, and comprehensive documentation. Ensure all models adhere to standards for accuracy, fairness, and reproducibility, and proactively address issues related to model drift, regulatory compliance, and ethical considerations in AI deployment. Requirements Bachelor's or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field; OR a Bachelor's degree with equivalent experience. 5-7 years of progressive experience in data science and machine learning. Quantitative Skills: Demonstrates a deep understanding of advanced statistical techniques, such as regression analysis, hypothesis testing, time series analysis, and multivariate statistics. Applies a broad range of machine learning algorithms—from supervised and unsupervised learning to ensemble methods and deep learning—to extract meaningful insights and drive data-driven decision-making across complex business challenges. Technical Skills: Possesses advanced proficiency in Python and/or R, leveraging these languages for data manipulation, statistical modeling, and deployment of machine learning solutions. Skilled in using modern ML and GenAI frameworks, such as scikit-learn for traditional models, TensorFlow and PyTorch for deep learning, and LangChain, Langgraph, openai, crewai, dspy, mlflow, etc., for building, orchestrating and evaluating generative AI applications. Experience includes developing and optimizing code, managing dependencies, and applying best practices in version control and containerization. GenAI Expertise: Hands-on experience implementing GenAI technologies, including large language models (LLMs) for natural language processing and understanding. Proficient in prompt engineering to fine-tune model outputs, utilizing retrieval-augmented generation (RAG) strategies to enhance responses with relevant knowledge, and integrating APIs to embed GenAI capabilities into production workflows and business applications. Data Management: Expertise in using SQL for querying, transforming, and aggregating data from relational databases. Demonstrates experience working with both structured data (e.g., tables, spreadsheets) and unstructured sources (e.g., text, images, documents), applying appropriate preprocessing and feature engineering techniques to ensure data quality and relevance for analytics and modeling. Problem-Solving: Exhibits excellent problem-solving skills, approaching challenges creatively and analytically. Capable of dissecting complex issues, identifying root causes, and designing innovative solutions. Frequently takes a fresh perspective on existing processes or models, independently developing and implementing strategies that improve efficiency, accuracy, or business value. Communication: Effectively communicates difficult or sensitive information to diverse stakeholders, translating complex technical concepts into clear, actionable insights for both technical and non-technical audiences. Skilled at facilitating discussions, presenting findings, and building consensus among cross-functional teams to drive project alignment and successful outcomes. Leadership: Serves as a force multiplier for the team by mentoring junior members, providing guidance on technical challenges, and sharing best practices in data science. Actively contributes to team knowledge-sharing, fostering a collaborative and growth-oriented environment that enhances overall team capability and performance. Business Acumen: Demonstrates a strong understanding of key business drivers, market dynamics, and organizational priorities. Applies data science expertise to identify opportunities for improvement, solve high-impact business problems, and deliver actionable insights that support strategic decision-making and value creation for the company. Preferred Ph.D. in a related quantitative field Experience in the life/health insurance or reinsurance industry. Experience working with Databricks, Snowflake, and AWS tech stacks. Experience working with large longitudinal datasets using actuarial methods of analysis #LI-SP2 #LI-REMOTE 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. We’re excited to get to know you and connect your unique skills with our global opportunities. To create a modern and seamless experience, we use artificial intelligence (AI) in parts of our preliminary screening process. This technology helps us personalize job recommendations, automate interview scheduling, evaluate candidates based solely on experience—without considering name, gender, or other personal details—and provide real-time answers through our chatbot. AI is used only during early screening and never makes hiring decisions. Your RGA recruiter will work closely with you every step of the way to ensure the process feels personal, thoughtful, and focused on you. Compensation Range: $126,710.00 - $188,840.00 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.