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.
