Data Science Actuary

Position Overview The Data Science Actuary is a qualified actuary with advanced analytics capabilities and visionary leader in our Americas Data Solutions team. In this pivotal role, you will harness cutting‑edge predictive modeling techniques and first‑class data assets to tackle complex challenges unique to the global reinsurance sector, directly impacting RGA's mission of making financial protection accessible and reliable. As a trusted architect of transformational solutions, you will spearhead cross‑functional initiatives, set the standard for technical excellence, and collaborate closely with RGA's senior leadership to shape and execute a data science strategy that drives business growth and redefines what's possible for our clients and partners worldwide. Responsibilities Strategic Solution Architecture: Spearhead the end‑to‑end design and architecture of sophisticated predictive models tailored to address the unique challenges and opportunities in the reinsurance sector. This includes leading statistical modeling initiatives and integrating these solutions into critical business functions like pricing, risk assessment, claims analytics, and customer engagement. Ensure that model architectures are robust, secure, and optimized for both performance and interpretability, while aligning with RGA's strategic objectives and compliance requirements. Technical Leadership & Mentorship: Provide hands‑on technical leadership by setting high standards for analytical rigor and solution quality. Mentor and coach both senior and junior data scientist actuaries, offering guidance on advanced modeling techniques, code review, and project management best practices. Foster a collaborative and innovative team environment that encourages knowledge sharing, continuous learning, and the adoption of new tools and methodologies. Lead technical deep‑dives and problem‑solving sessions to address the most complex analytical challenges faced by the team. Cross‑Functional Leadership: Oversee and coordinate large‑scale, cross‑functional projects that require collaboration across diverse groups such as Actuarial, Underwriting, IT, Legal, and Operations. Develop and maintain project plans, set clear deliverables, and facilitate effective communication among stakeholders to ensure alignment on project goals and timelines. Proactively identify and resolve conflicts or bottlenecks, ensuring that projects are executed efficiently and deliver measurable business impact. Innovation & Strategy: Act as a catalyst for innovation by proactively scouting, evaluating, and piloting emerging modeling techniques and technologies relevant to the reinsurance industry. Lead the identification of new use cases, prototype novel solutions, and conduct feasibility studies. Recommend and implement best practices for integrating advanced analytics into business processes, and contribute to the development and ongoing refinement of the data science team's strategic roadmap. Share insights and learnings through presentations, white papers, and thought leadership within and beyond the organization. Governance & Best Practices: Develop, document, and enforce best practices for data analysis, model development, and model governance. This includes establishing protocols for model validation, monitoring, versioning, and lifecycle management, as well as ensuring compliance with ethical standards and regulatory requirements. Oversee the creation and maintenance of comprehensive technical documentation to support transparency, reproducibility, and knowledge transfer across the team and organization. Senior Stakeholder Management: Build and maintain strong partnerships with senior business leaders to understand strategic priorities, identify and prioritize high‑impact analytical opportunities, and drive the adoption of data‑driven solutions. Communicate complex technical concepts in clear, actionable terms tailored to both technical and non‑technical audiences. Lead negotiations and change management efforts to overcome resistance and ensure successful implementation and value realization of new data science initiatives. Domain Expertise: Apply deep subject matter expertise in life and health reinsurance‑including domains such as mortality, morbidity, underwriting, and claims‑to inform the design and interpretation of predictive models. Ensure that all analytical solutions are contextually relevant, aligned with industry best practices, and capable of addressing real‑world business problems. Serve as a key resource for translating business needs into technical requirements and for validating model outputs against domain benchmarks and regulatory standards. Requirements Bachelor's degree in Mathematics, Finance, Statistics, Actuarial Science, or related field FSA Accreditation 7+ years of actuarial experience 3+ years statistical modeling experience for insurance or related applications (GLM, Decision Trees, Time Series, Regression, etc.) Quantitative Skills: Possesses expert‑level knowledge in a wide range of statistical modeling techniques, including regression analysis, classification, clustering, time series forecasting, and survival analysis. Possesses expert‑level knowledge of traditional actuarial analysis, including multivariate analysis, experience studies, pricing, and valuation. Strong ability to apply advanced analytics techniques to traditional actuarial problems. Technical Skills: Exhibits deep, hands‑on expertise in programming languages such as Python and R for data analysis, machine learning, and automation. Highly proficient in SQL for complex data extraction, transformation, and loading (ETL) processes. Demonstrated ability to deploy scalable models in cloud environments such as AWS, Azure, and Databricks, including the use of containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for robust production deployments. Problem‑Solving: Demonstrated ability to approach highly complex and ambiguous problems with creativity and rigor. Skilled at framing business challenges as analytical questions, designing innovative solutions that may lack existing precedent, and validating approaches through experimentation and stakeholder feedback. Adept at balancing technical feasibility with business impact to deliver actionable insights. Communication & Negotiation: Exceptional written and verbal communication skills, with the ability to distill complex technical concepts into clear, compelling narratives for both technical and non‑technical audiences. Experienced in presenting findings to senior leadership, influencing decision‑making, and negotiating with external partners to drive alignment and adoption of data‑driven strategies. Skilled at facilitating cross‑functional discussions and overcoming resistance to change. Leadership: Demonstrated success in leading and inspiring cross‑functional teams of data scientists, engineers, actuaries, and business stakeholders. Adept at managing projects from initial concept through to production deployment, ensuring alignment with organizational goals and timelines. Committed to mentoring and developing junior data scientist actuaries, fostering a collaborative and innovative team environment. Business Acumen: Strong understanding of reinsurance treaty structures, risk assessment, and underwriting processes, with the ability to apply actuarial concepts to inform data science solutions. Experienced in quantifying and articulating the business value of analytics initiatives, translating technical outputs into actionable business recommendations that drive profitability and operational efficiency. Preferred Master's or Ph.D. in Data Science, Computer Science, Statistics, Actuarial Science, Mathematics, or a related quantitative field. Publications or presentations in relevant data science, actuarial, or industry conferences. Experience working with Databricks, Snowflake, and AWS tech stacks. Experience working with large longitudinal datasets using actuarial methods of analysis GenAI Expertise: Proven experience in developing, fine‑tuning, and evaluating large language models (LLMs) to address specific business requirements. Skilled in managing MLOps pipelines to ensure seamless integration of AI solutions into production, including monitoring, versioning, and automated retraining. Adept at building production‑grade AI systems that are reliable, explainable, and compliant with governance standards, enabling the deployment of advanced AI applications in regulated industries. Demonstrates advanced proficiency in designing, validating, and interpreting machine learning algorithms (such as tree‑based models, ensemble methods, neural networks, and natural language processing). Maintains up‑to‑date expertise in Generative AI (GenAI) technologies, including prompt engineering, model evaluation, and the application of large language models (LLMs) to solve business challenges. Experienced in leveraging modern machine learning and GenAI frameworks (e.g., TensorFlow, PyTorch, Hugging Face, scikit‑learn) to develop, test, and optimize models. Benefits 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 $135,850.00 - $207,700.00 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. Equal Opportunity Employment 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. #J-18808-Ljbffr RGA Reinsurance Company