Assistant Vice President, Health Analytics Data Scientist

Hands-on Assistant Vice President role supporting US Health Analytics through causal inference, predictive modeling, and end-to-end model governance.

• Develop and deliver causal inference and predictive modeling work streams for the US Health Analytics practice, maintaining methodological rigor while building, reviewing, validating models and code within project timelines
• Partner with Aon Business Solutions / Technology (ABS), cloud architects, and offshore engineering and data science teams to align on data pipelines, model architecture, and deployment standards
• Coordinate delivery across locations and time zones, and move models from development into scalable, supported production
• Design, build, and tune tabular analyses and predictive models using gradient-boosted methods, statistical approaches, and emerging AI techniques for use cases such as:
  • Cost and utilization forecasting
  • High-cost claimant prediction
  • Risk stratification
  • Program engagement propensity
• Work with large eligibility, medical claims, and pharmacy claims datasets from multiple sources
• Design and execute medical economics and causal inference studies estimating program, treatment, vendor, and point-solution impact on financial, utilization, and clinical outcomes
• Apply matched cohort target trial emulation, including explicit eligibility, time zero, and follow-up definitions with propensity score matching or weighting
• Use difference-in-differences and event-study designs, including parallel-trends and sensitivity testing, plus risk adjustment
• Document methods, assumptions, data definitions, and results thoroughly (methodology write-ups, technical appendices, model cards, reproducible analysis records)
• Present findings, uncertainty, and limitations clearly to clients, carriers, consultants, and senior leaders via polished reports and presentations
• Ensure MLOps and model governance end-to-end: experiment tracking, model registries, reproducible training pipelines, automated testing, CI/CD, drift and performance monitoring, and documentation, validation, and approval workflows aligned with Aon risk, privacy, and compliance standards
• Serve as a senior technical, code, and methodology reviewer; set standards for model validation, explainability, bias and fairness review, and quality control
• Collaborate with senior leaders across Aon health actuarial, consulting, clinical, innovation, and specialty practice areas; stay current on emerging vendors and startups in healthcare navigation, digital health, and care delivery
• Mentor and coach data scientists; lead multiple projects with clear priorities, timelines, and milestones

• Minimum 7 years of total experience, including health insurance, with a track record of delivering value to clients and colleagues
• Strong foundation in applied statistics or medical economics, including model training and validation, statistical testing, regression and generalized linear models, and experimental and quasi-experimental design
• Deep hands-on experience building, tuning, and interpreting tabular predictive models, including feature engineering, cross-validation, hyperparameter optimization, calibration, class imbalance, and explainability methods
• Hands-on experience applying causal inference to observational health data / medical economics, including:
  • Matched cohort target trial emulation
  • Propensity score methods
  • Difference-in-differences (including staggered-adoption and event-study approaches)
  • Sensitivity analysis
  • Risk adjustment
• Detailed understanding of US health data: eligibility, medical and pharmacy claims, and coding standards including ICD-10-CM
  , ICD-10-PCS
  , CPT
  , and DRG
• Experience with MLOps and model governance (for example, MLflow or similar), including model registries, automated…