"HMS - Postdoctoral Fellow in Health Care Policy - Statistical Methods"

Position Description

The Department of Health Care Policy at Harvard Medical School seeks a highly motivated postdoctoral fellow to join a PCORI-funded project developing new methods to improve the transparency of individualized treatment effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy researchers.

The successful candidate will lead development of variable importance measures – including the design, implementation, and evaluation of new methods for explaining individualized treatment effect predictions and serving as author on methodological publications. Other work will include running simulation studies, applying the methods to electronic health records (EHR) and clinical trial datasets, and collaborating on dissemination through open-source R packages and associated tutorials.

This position provides an opportunity to produce high-impact methodological research while engaging in applied health policy contexts.

Basic Qualifications

• Ph.D. in areas such as statistics, biostatistics, computer science, economics, or a related quantitative field
• Strong skills in R and/or Python and experience analyzing complex data
• Clear scientific writing and communication; a track record of publications
• Experience with causal inference
• Bonus: experience with explainable ML, optimization/decision strategies, or work with EHR/clinical trial data

Additional Qualifications

With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes of collective bargaining and matters affecting your compensation and working conditions.

Contact Information

Mary Hurley

Contact Email: hurley@hcp.med.harvard.edu

Special Instructions

Required documents:

• CV
• Research summary of PhD work
• Cover letter describing your interest in the lab and initial ideas for new research directions or projects you could undertake in the lab.
• Names and contact information for two people who have agreed to serve as a reference

Please submit application documents through the ARIeS portal. Applications will be reviewed starting December 1, 2025, and on a rolling basis thereafter until the position is filled. Selected candidates will be invited for interviews involving a research presentation on past work.

Start date: The position is expected to start on or around July 1, 2026, with flexibility to start earlier. Duration: This is a one-year term position from the date of hire, with the possibility of a second-year contingent upon work performance, business need, and continued funding to support the position.