About the Company
A fast-growing healthcare benefits company is transforming how high-cost specialty therapies are delivered and financed. The business helps employers reduce specialty pharmacy and infusion costs by removing hidden markups, expanding access to high-quality care, and creating greater transparency around treatment pricing.
The company partners with major employers and benefits organizations, helping improve patient access while delivering meaningful cost savings.
About the Role
They’re hiring a Head of Data Science & Analytics to own the scientific and analytical direction of the business.
This is a hands-on leadership role focused on using complex healthcare data to identify patients, forecast treatment demand, measure program impact, and build the analytical infrastructure that supports a rapidly scaling organization.
You’ll work across medical and pharmacy claims, eligibility, clinical, financial, and operational data to:
Build predictive models around patient identification, treatment initiation, adherence, and engagement
Forecast specialty therapy utilization and demand across populations
Develop rigorous methodologies for measuring savings and patient outcomes
Design experiments and observational analyses to understand intervention impact
Define and standardize core business metrics across the organization
Build scalable reporting and self-service analytics
Establish automated data-quality, validation, and reproducibility standards
Partner directly with Finance, Commercial, Clinical, Product, and Operations leadership
Eventually hire and build out the data science and analytics function
Ideal Background
Strong foundation in statistics and applied data science
Hands-on SQL and Python
Experience working with claims, eligibility, payer, PBM, pharmacy, or other longitudinal healthcare data
Experience with forecasting, causal inference, survival analysis, Bayesian/hierarchical modeling, simulation, or experimentation
Comfortable dealing with messy, incomplete, and delayed healthcare data
Has defined important business metrics rather than simply reporting them
Strong data-quality and validation mindset
Experience building or leading data science/analytics capabilities
Wants to remain hands-on , especially initially
Bonus for dbt, Omni, Looker, semantic layers, or self-service analytics
Bonus for experience in a fast-growing company