Senior Data Scientist

The Senior Data Scientist at RGA plays a pivotal role in building and shipping to production 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, implements, and operates in production sophisticated analytical models to solve high-impact business challenges, powering RGA’s data-driven transformation. By collaborating closely with business and actuarial stakeholders, the Senior Data Scientist translates complex risk and market insights into deployed, monitored solutions, mentors emerging talent, and ensures RGA remains at the forefront of predictive analytics and competitive advantage.

Responsibilities

• Ship GenAI/agent systems to production (primary). Lead the end-to-end development, implementation, and deployment of generative AI and agentic solutions, leveraging large language models (LLMs) and tool-using agents 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, deploying them into a live, business-used workflow and owning them once running, and ensuring responsible AI practices such as bias mitigation and transparency.
• End-to-end ML modeling. Design, develop, and deploy ML models for mission-critical problems — underwriting automation, pricing optimization, claims analytics — including requirements, feature engineering, model selection and tuning, and integration into production environments.
• Production operations & MLOps. Own the deployed lifecycle of your solutions: CI/CD, versioning, monitoring, evaluation, and retraining. Detect and resolve model drift and regression. Treat “deployed” as the start of the work, not the finish.
• Data pipeline architecture. Build and maintain robust, automated data pipelines and ETL in partnership with data engineering — scalable ingestion, transformation, and validation for large, complex datasets.
• Technical leadership & mentorship. Serve as a technical authority and force multiplier: conduct code reviews, set production-quality standards, mentor junior data scientists, and share knowledge of emerging techniques.
• Project leadership. Lead and manage small-scale projects — defining scope and objectives, developing project plans, allocating resources, and coordinating activities across cross-functional teams. Maintain proactive stakeholder communication to track progress, address risks, and ensure timely, successful delivery aligned with business goals.
• Stakeholder communication. Translate complex analytical results into clear, actionable insight for business leaders and senior management; drive data-driven decisions through visualization and storytelling.
• Responsible AI & 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 everything that reaches production.

Requirements

• Bachelor's or Master's in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field; or a Bachelor's with equivalent experience.
• 5–7 years of progressive data science and machine learning experience.
• Demonstrated ownership of at least one GenAI or ML system that the candidate personally took to production and operated — deployed, used by the business, and maintained post-launch.
• Production GenAI (the differentiator). Hands-on experience building, fine-tuning, and deploying GenAI technologies, including large language models (LLMs) and tool-using agents 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, orchestrating tools and agents, and integrating APIs to embed GenAI capabilities into production workflows and business applications. Can speak concretely to evaluation, guardrails, and what broke in production and how they fixed it.
• MLOps & deployment. Real experience with CI/CD for models, containers, version control, monitoring, and automated retraining — not just notebook experimentation.
• 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 or equivalent agent frameworks for building and orchestrating generative AI applications. Experience includes developing and optimizing code, managing dependencies, and applying best practices in version control and containerization.
• 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

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