Core Job Responsibilities

Predictive Modeling and Risk Assessment

• Develop mortality and morbidity models: Build advanced statistical models to predict life expectancy, disease onset, and disability timelines across diverse population segments.
• Refine underwriting guidelines: Analyze historical healthcare data, medical records, and clinical trial results to help underwriters more accurately evaluate applicants with pre-existing conditions.
• Incorporate emerging health trends: Adjust risk models to account for modern medical advancements, public health shifts, and changing lifestyle behaviors (e.g., vaping, wearable health tech data).

Mortality and Morbidity Experience Studies

• Analyze historical claims data: Track and evaluate actual claims against expected claims to identify specific risk variations or anomalies.
• Perform cohort tracking: Monitor specific blocks of policyholders over time to detect early trends in health deterioration or longevity improvements.
• Publish internal benchmarks: Create localized baseline tables for critical illnesses, disability, and long-term care events.

Advanced Data Analytics and Engineering

• Manage complex healthcare datasets: Extract, clean, and consolidate disparate data sources, including Electronic Health Records (EHRs), prescription histories, and public health registries.
• Deploy predictive analytics tools: Leverage programming languages like R, Python, or SAS to build automated machine learning pipelines for automated underwriting or fraud detection.
• Integrate Big Data: Source and evaluate alternative external data sources to enhance the predictive power of existing proprietary datasets.

Cross-Functional Collaboration & Strategic Support

• Partner with Actuaries: Provide the foundational statistical frameworks, population metrics, and risk probabilities that product actuaries require to price new products accurately.
• Advise Product Development: Use health demographic insights to identify underinsured segments and help design niche health, critical illness, or longevity insurance products.

Leadership and Communication

• Translate data into strategy: Present highly technical statistical conclusions into clear, actionable business recommendations for executive leadership and stakeholders.
• Mentor junior analysts: Oversee the daily tasks, code reviews, and technical growth of junior data analysts and statisticians within the risk department.

Core Job Requirements Education and Qualifications

• Advanced Degree: Master's degree or Ph.D. in Biostatistics, Statistics, Epidemiology, Public Health, Data Science, or a highly quantitative field.

Technical and Programming Skills

• Statistical Programming: Expert proficiency in R, Python, or SAS for data manipulation, predictive modeling, and statistical testing.
• Database Management: Strong command of SQL to extract, query, and merge massive, unstructured datasets.
• Machine Learning: Practical experience deploying predictive algorithms, survival analysis models, regression analysis, and longitudinal data modeling.
• Data Visualization: Competency in building executive dashboards using tools like Tableau, Power BI, or Shiny.

Domain Knowledge and Experience

• Professional Experience: Typically 8 to 10+ years of experience working with health data, preferably within life insurance, health insurance, reinsurance, or healthcare consulting.
• Healthcare Data Expertise: Deep familiarity with clinical datasets, including Electronic Health Records (EHRs), medical billing codes (ICD-10, CPT), prescription histories, and clinical trial outcomes.

Compliance and Soft Skills

• Communication: Ability to translate highly technical data science and epidemiological concepts into clear, actionable business strategies for non-technical executives.
• Leadership: Proven track record of mentoring junior analysts and leading cross-functional project teams.