Staff Data Scientist, Healthcare Economics
New
P
Pomelo CareHealthcare economics
Fully remote work flexibility (within the US)Full-TimeStaff
SalaryThe expected base salary range offered for this role is $220,000 - $250,000. This role is also eligible for equity
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Job Details
- Experience
- 6+ years professional experience
- Required Skills
- PythonSQLR
Requirements
- Have 6+ years of professional experience developing statistical models for complex healthcare problems.
- Have experience with varied techniques such as propensity score matching, linear and logistic regressions, k-means clustering, difference-in-differences, or decision trees.
- Have experience with cost-of-care or clinical-outcomes target setting at a payer, managed care organization, integrated delivery network, value-based care provider, or healthcare consulting firm.
- Have experience presenting statistical analyses to external stakeholders with varied technical expertise.
- Be proficient in Python or R.
- Be proficient in SQL.
- Formal training in statistics, econometrics, or actuarial science is a bonus.
- Experience with risk-based or value-based contracting, opportunity sizing, and ROI modeling is a bonus.
- Experience with medical claims data is a bonus.
- Peer-reviewed publications in health outcomes or health economics are a bonus.
- Experience with data dashboarding or visualization tools such as Looker, Tableau, or Metabase is a bonus.
- Experience working with dbt is a bonus.
Responsibilities
- Break large, ambiguous mandates into definitions, assumptions, and key questions with stakeholders.
- Design and execute scalable, repeatable analyses of Pomelo Care program outcomes, extending or developing methods as needed.
- Present directly to external partners, align on evaluation methodology and parameters, and build credibility with evaluation teams.
- Drive evidence strategy through partner-facing reporting, conference presentations, and peer-reviewed publications.
- Build data tables, pipelines, and dashboards that combine health data sources and make insights accessible across teams.
- Identify opportunities to scale and automate insights with data and analytics engineering teams.
- Define statistical and health economics evaluation methods, mentor data scientists, and guide technical investment.
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