Senior Data Scientist

Reinsurance Group of America, Incorporated•Chesterfield, MO

2d•$126,710 - $188,840•Remote

About The Position

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.

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.
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.
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.
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.
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.
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.
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.
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.
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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.
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.