Senior Data Scientist

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.