Lead Data Scientist, Actuarial & Advanced Risk Analytics (Healthcare)

Fractal is a strategic AI partner to Fortune 500 companies, focusing on enhancing human decision-making through intelligent systems. The Lead Data Scientist will lead the development of actuarial and risk models within the US healthcare system, utilizing advanced analytics and AI to drive business outcomes and improve healthcare insights.

Responsibilities:

• Design, build, and enhance actuarial models related to medical cost forecasting, utilization risk, trend analysis, and financial projection
• Develop risk adjustment, severity, and morbidity models for Medicare, Medicaid, and Commercial populations
• Lead modeling efforts for cost-of-care, member risk stratification, high-cost claimant prediction, and population health risk scoring
• Apply proven actuarial methodologies while incorporating advanced statistical and machine learning techniques where appropriate
• Partner with Data Science and AI teams to translate actuarial models into scalable analytical and AI solutions
• Evaluate and guide the use of ML approaches (GLMs, GAMs, gradient boosting, survival analysis, time series, etc.) alongside actuarial methods
• Ensure explainability, stability, and regulatory readiness of risk models used in AI-driven workflows
• Support development of predictive and prescriptive analytics used in Payment Integrity, care management, utilization management, and financial risk mitigation
• Perform deep analysis across claims, member, and provider datasets to identify cost drivers, risk concentration, and leakage
• Quantify financial impact of payment programs, adjudication rules, benefit design, and provider behavior
• Support Payment Integrity (PI) use cases including duplicate claims, pricing anomalies, eligibility issues, and reimbursement accuracy
• Partner with finance and actuarial teams on budgeting, reserving, and forecasting exercises
• Act as a domain authority for actuarial and healthcare risk concepts within data science and AI initiatives
• Translate complex analytical results into clear, executive-ready insights and recommendations
• Guide analysts and data scientists on actuarial standards, modeling assumptions, and validation techniques
• Support regulatory, audit, and compliance reviews through transparent documentation and defensible methodologies

Requirements:

• 10+ years of experience developing actuarial, risk, or financial models in the US healthcare industry
• Strong background in one or more segments: Medicare (MA, FFS, Risk Adjustment), Medicaid / State programs, Commercial / Employer plans
• Proven experience with cost modeling, utilization forecasting, trend analysis, and member risk stratification
• Deep understanding of claims adjudication, benefit design, pricing, and reimbursement mechanics
• Advanced statistical modeling expertise (GLMs, regression, survival models, time series)
• Strong hands-on experience with analytical tools and languages (Python, R, SQL, or similar)
• Ability to work with large, complex healthcare datasets (claims, eligibility, provider, utilization)
• Experience collaborating with data science teams using modern analytics platforms (Databricks or similar preferred)
• Exceptional analytical thinking and problem-solving ability
• Strong communication skills with the ability to explain complex actuarial concepts to technical and non-technical audiences
• Demonstrated ability to influence decisions at senior leadership levels
• Actuarial credentials (ASA, FSA) or significant progress toward certification
• Experience supporting AI, advanced analytics, or digital transformation initiatives
• Exposure to Payment Integrity, FWA analytics, care management, or utilization management programs
• Experience with forecasting dashboards, scenario modeling, or executive financial reporting
• Familiarity with Call Center datasets (member & provider interactions), Provider RCM data, and/or EHR/clinical data for integrated risk analysis