Postdoctoral Researcher - Health Services and Health Economics Research

Postdoctoral Researcher
- Health Services and Health Economics Research

Location: University of Pennsylvania
- Perelman School of Medicine

Open Date: Jul 27, 2026

Faculty Mentor: Dr. Michael Harhay

Department: Biostatistics, Epidemiology & Informatics

Number of Positions: 1

Open to applications from U.S. citizens and foreign nationals.

The research groups of Drs. Scott Halpern, Michael Harhay, and Ari Friedman at the University of Pennsylvania invite applications for a postdoctoral researcher in health services research (HSR) and health economics. Individuals with >1 year of post-doctoral experience could be considered for research associate roles. We would be particularly interested in (bio) statisticians with training and interests in health economics and cost-effectiveness research.

The postdoc researcher will support and lead projects using large, complex electronic health record (EHR) datasets, ranging from short, focused descriptive studies to natural experiments and causal inference. The position suits a candidate with prior experience using healthcare data (ideally including from electronic health records) who combines skills in a variety of econometric and statistical methods, technical sophistication in deploying those methods rigorously, curiosity, and the ability to work with clinicians and engage with clinical literature to inform their work.

The role places equal value on ambitious analysis and on the day-to-day discipline of data cleaning and documentation that underpins credible findings.

The ideal candidate combines strong quantitative training with sound judgment about when data can and cannot answer a question. Working with colleagues fluent in both clinical practice and rigorous quantitative methods, the postdoc researcher will learn to distinguish mechanical features of healthcare data from substantive economic and meaningful findings and to exploit those features to strengthen causal inference.

• Develop estimation strategies that use multiple sources of plausibly exogenous variation to identify clinically and economically relevant quantities of interest.
• Investigate natural experiments in the emergency department and ICU tied to fundamental economic questions, supported by additional clinical and operational data-gathering infrastructure.
• Explore distributed regression analyses across a coalition of health systems on emergency department boarding and crowding.
• Contribute statistical and cost-effectiveness expertise to ongoing and planned studies across the three research groups.
• Lead manuscript writing aimed at leading medical journals, in addition to health services research outlets.
• Present findings at conferences.

• Clean and structure EHR data for analyses.
• Maintain careful and reproducible documentation of datasets/code and lead the creation of an organized catalog of existing datasets.
• Record methodological decisions in detail and use those notes to draft the Methods sections of manuscripts.
• Write data-sharing and replication plans.
• Prepare cuts of cleaned datasets for rotating students on an occasional basis.

• Serve as a point of contact between students in the lab and the faculty, assigning weekly tasks, monitoring progress, and supporting students' development.
• Maintain coding infrastructure for weekly reports tracking study progress and data completeness across active projects.
• Coordinate co-author feedback and track manuscript submissions.
• Develop and update training guides, study manuals, and materials supporting consistent research operations.

• Doctoral degree in biostatistics, statistics, economics, epidemiology, health services research, or a related quantitative field, ideally with training or demonstrated interest in economics and cost-effectiveness analysis.
• Experience working with healthcare data and large relational datasets, with an understanding of the level of rigor the work requires.
• Proficiency in a statistical programming language such as R or Python.
• Skill in data cleaning and management, statistical and econometric analysis (including cost-effectiveness methods), manuscript writing…