Staff Data Scientist, Healthcare Economics
New
P
Pomelo CareHealthcare economics
Location: United States; Workplace: Remote; 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, giving you an ownership stake in Pomelo’s mission.
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Job Details
- Experience
- 6+ years professional experience
- Required Skills
- PythonSQLdbtR
Requirements
- 6+ years of professional experience developing statistical models to answer complex healthcare problems.
- Experience using varied statistical techniques, including propensity score matching, linear and logistic regressions, k-means clustering, difference-in-differences, or decision trees.
- 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.
- Experience presenting statistical analyses to external stakeholders with varied technical expertise.
- Proficiency in Python or R and SQL.
- Formal training in statistics, econometrics, or actuarial science.
- Experience with risk-based or value-based contracting, including opportunity sizing and ROI modeling.
- Experience working with medical claims data.
- Experience with data dashboarding or visualization, such as Looker, Tableau, or Metabase.
- Experience working with dbt.
- Peer-reviewed publications in health outcomes or health economics.
Responsibilities
- Break ambiguous mandates into definitions, assumptions, and key questions in alignment with stakeholders.
- Architect and execute scalable, repeatable analyses assessing the outcomes of Pomelo Care programs.
- Extend existing evaluation approaches or build new methods, and stand behind methodological choices under scrutiny.
- Present directly to external partners, align on methodology and parameters, and build credibility with evaluation teams.
- Drive evidence strategy across partner-facing reporting, conference presentations, and peer-reviewed publications.
- Build data tables, pipelines, and dashboards that combine diverse health data sources and make insights accessible across teams.
- Identify opportunities to scale and automate insights with data and analytics engineering teams.
- Define evaluation methodologies, mentor other data scientists, and guide technical investment.
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