Assistant Vice President, Health Analytics Data Scientist
AON Corporation · Seattle, WA, United States ·
COMPENSATION
€•• – ••k
Listed on WeAreDevelopers
- 7+ years experience
- US Health Data focus
- Python and R required
- MLOps and Governance
- Causal Inference expert
You will design and validate statistical algorithms and machine learning models to help employers understand healthcare costs and risks. This position focuses on total-cost-of-care forecasting and evaluating health program impacts using rigorous causal inference methods.
Working with actuaries, consultants, and engineering teams, you will move models from development into governed, monitored production environments. You will also mentor junior scientists and ensure methodological rigor across analytics deliverables.
What you'll do
- Develop causal inference and predictive modeling workstreams for US Health Analytics
- Collaborate with cloud architects and offshore teams to deploy scalable models
- Build tabular analyses for cost forecasting and risk stratification on claims data
- Execute medical economics studies using matched cohort and difference-in-differences designs
- Document methods and present findings to clients and senior leaders clearly
- Ensure end-to-end MLOps including experiment tracking and drift monitoring
- Review code and methodology for junior data scientists and set quality standards
- Mentor team members and lead multiple projects with defined timelines
What you'll need
- Minimum 7 years of experience in health insurance or benefits consulting
- Strong foundation in applied statistics, regression, and experimental design
- Deep hands-on experience building and tuning tabular predictive models
- Expertise in causal inference methods like propensity score matching
- Detailed understanding of US health data standards such as ICD-10 and CPT
- Experience with MLOps tools like MLflow and model governance processes
- Proficiency in Python and/or R with strong SQL skills
- Bachelor's or master's degree in a quantitative field or equivalent experience
Nice to have
- Experience with Databricks, Spark, Azure, Docker, and CI/CD pipelines
- Advanced degrees in relevant quantitative fields
- Track record of partnering with distributed engineering teams
Stack
- Python
- R
- SQL
- Databricks
- Apache Spark
- Microsoft Azure
- Docker
- MLflow
How you'll work
- Role involves working with offshore and distributed engineering teams
- Coordination across multiple locations and time zones required