Health Actuarial, Data Analyst

Position Summary

The Actuarial/Data Analyst is part of an internal resource team that develops models and analyzes health care data, including cost transparency data, across the book of business to identify cost, utilization, and population health trends. The role builds expertise in relevant data sources, methodologies, and analytical tools; interprets results; and translates findings into actionable insights and resources for actuarial consultants and other client-facing teams. The analyst collaborates with internal stakeholders and external vendor partners to strengthen the team’s data capabilities and support consistent, informed analysis.

Key Responsibilities\:

· Data Extraction and Management\: Extract, validate, and manage large, complex datasets from internal and external sources for use in book-of-business analyses and model development.

· Health Care Cost Transparency\: Analyze health care cost transparency data and support related tools; develop expertise in the data, methodologies, limitations, and applications; and serve as the primary resource for internal teams and liaison to the external vendor partner.

· Book-of-Business Analysis\: Analyze health plan performance across the book of business, identifying and interpreting trends in medical and pharmacy costs, utilization patterns, and population health to support client-facing teams.

· Analytical Insights\: Interpret data and model results to identify meaningful patterns, explain key cost and utilization drivers, and develop actionable insights for internal stakeholders.

· Stakeholder Collaboration\: Partner with actuarial consultants, senior analysts, and other client-facing teams to understand business needs, define analytical approaches, and communicate findings that support client strategy and decision-making.

· Analytical Communication\: Summarize methodologies, findings, and implications through reports, presentations, dashboards, and other resources designed for technical and nontechnical audiences.

· Quality Assurance\: Ensure the accuracy, validity, and integrity of data and analytical outputs while meeting timelines and maintaining high-quality standards.

· Process Improvement\: Identify and support opportunities to automate reporting and improve data collection, modeling, and analysis processes.

· Predictive Modeling\: Develop models for emerging business needs and estimate cost and utilization changes across the book of business. Adapt models for client-specific applications as requested by client-facing teams.