Assistant Vice President, Health Analytics Data Scientist

Assistant Vice President, Health Analytics Data Scientist Aon is looking for an Assistant Vice President, Health Analytics Data Scientist. Preferable locations are Chicago, IL; Atlanta, GA; Denver, CO; Seattle, WA but open to other Aon locations or possibly remote for the right fit. We are seeking a hands-on, statistically grounded health data scientist, health actuary or medical economics expert to work on cost-of-care measurement, causal inference and predictive modeling workstreams and development of Aon analytics capabilities. You will own the design, validation, documentation, and delivery of the statistical and machine learning / predictive modeling algorithms and analyses that help employers and their partners understand cost, risk, and the impact of health programs – including total-cost-of-care predictive modeling and causal evaluations of programs and vendors using matched cohort trial emulation, difference-in-differences, and risk adjustment. You will work to evolve capabilities and model performance, review code and models, document and present results to clients and senior leaders, and take models from development into governed, monitored production, working closely with actuaries, consultants, clinical and innovation specialists, Aon Business Solutions / Technology (ABS), technology architects, and engineering teams. The ideal candidate has 7+ years of experience including commercial health insurance and/or health benefits consulting, a strong foundation in applied statistics and causal inference, and a track record of building and deploying tabular predictive models on large medical and pharmacy claims datasets. The ability to document methods rigorously and present results clearly to technical and non-technical audiences is critical. Practical experience with MLOps and model governance, and with partnering with enterprise technology and distributed engineering teams to move models into scalable, supported production, is essential. Aon is in the business of better decisions. At Aon, we shape decisions for the better to protect and enrich the lives of people around the world. As an organization, we are united through trust as one inclusive team and we are passionate about helping our colleagues and clients succeed. What the day will look like: Develop and deliver causal inference and predictive modeling workstreams for the US Health Analytics practice, ensuring methodological rigor while staying hands-on in building, reviewing, and validating models and code within project timelines Collaborate closely 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 including gradient-boosted, statistical methods and emerging AI modeling techniques– such as cost and utilization forecasting, high-cost claimant prediction, risk stratification, and program engagement propensity – on large eligibility, medical claims, and pharmacy claims datasets from multiple sources Design and execute medical economics and causal inference studies that estimate the impact of programs, treatments, vendors, and point solutions on financial, utilization, and clinical outcomes, using matched cohort target trial emulation (explicit eligibility, time zero, and follow-up definitions; propensity score matching or weighting), difference-in-differences and event-study designs with parallel-trends and sensitivity testing, and risk adjustment Document methods, assumptions, data definitions, and results thoroughly – methodology write-ups, technical appendices, model cards, and reproducible analysis records – and present findings, uncertainty, and limitations clearly to clients, carriers, consultants, and senior leaders through polished reports and presentations Ensure MLOps and model governance end-to-end, including experiment tracking, model registries, reproducible training pipelines, automated testing, CI/CD, drift and performance monitoring, and model documentation, validation, and approval workflows aligned with Aon's risk, privacy, and compliance standards Serve as senior technical, code, and methodology reviewer for analytics deliverables, and set standards for model validation, explainability, bias and fairness review, and quality control Partner with senior leaders across Aon's health actuarial, consulting, clinical, innovation, and specialty practice areas, and stay current on emerging vendors and startups in healthcare navigation, digital health, and care delivery Mentor and coach data scientists, and lead multiple projects with clear priorities, timelines, and milestones Skills and experience that will lead to success: 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, and risk adjustment, with financial, utilization, and clinical outcomes metrics Detailed understanding of US health data, including eligibility, medical and pharmacy claims, and coding standards such as ICD-10-CM, ICD-10-PCS, CPT, DRG, and related industry standards Experience with MLOps and model governance – such as MLflow or similar tooling, model registries, automated training and scoring pipelines, version control, drift and performance monitoring, and model risk documentation and review Experience partnering with enterprise technology and architecture teams and working with offshore or distributed engineering teams to deploy and support models in production Expert Python and/or R and strong SQL; Databricks, Spark, Azure, Docker, and CI/CD experience a plus High-performing individual contributor who can mentor junior data scientists and work independently across multiple projects Excellent written and verbal communication, with a track record of documenting methods and assumptions clearly and presenting statistical results, uncertainty, and limitations to actuaries, consultants, clients, and non-technical audiences