Postdoctoral Associate

About the Role

The Department of Global Health at the Boston University School of Public Health (BUSPH) is seeking a Postdoctoral Associate to join the research team of Dr. Alana T. Brennan. This position focuses on a newly funded NIH R01 examining the real-world effectiveness, safety, and cost-effectiveness of glucagon-like peptide-1 receptor agonists (GLP-1RAs) among people with HIV and type 2 diabetes. The study utilizes Komodo Health's national longitudinal medical and pharmacy claims data, linked with clinical and laboratory information, to evaluate cardiovascular–kidney–metabolic (CKM) outcomes associated with GLP-1RA treatment. The Postdoctoral Associate will primarily contribute to the epidemiologic and causal inference components of the study. This role is suited for an early-career epidemiologist interested in applying modern epidemiologic and causal inference methods to complex, large-scale real-world healthcare data.

Key Responsibilities

• Contribute to the epidemiologic and causal inference components of the study.
• Collaborate across all three aims of the R01 project.

Requirements & Tech Stack

• Doctoral degree (PhD, ScD, DrPH, or equivalent) in epidemiology or a closely related quantitative population health field.
• Strong methodological training in epidemiologic study design and causal inference.
• Demonstrated experience applying causal inference methods to observational data.
• Strong quantitative and statistical programming skills in Stata, SAS, and/or R.
• Experience analyzing large, longitudinal observational datasets.
• Strong scientific writing and oral communication skills.
• A record of peer-reviewed publications appropriate to career stage.
• Ability to work both independently and collaboratively within a multidisciplinary research team.
• Prior experience in HIV, diabetes, or cardiometabolic research is not required.

Preferred Qualifications

• Experience in target trial emulation, including new-user, active-comparator study designs.
• Experience with advanced causal inference methods, including propensity score methods, inverse probability weighting, marginal structural models, and methods for time-varying exposures and confounding.
• Experience analyzing large U.S. administrative medical and pharmacy claims data or similarly complex longitudinal healthcare datasets.
• Experience with SQL for database management and querying large datasets is highly desirable.
• Experience with code optimization, parallelization, and/or other approaches for efficiently analyzing very large healthcare datasets.
• Experience with survival and time-to-event analysis, including competing-risk methods.
• Experience with causal mediation analysis.
• Experience in the development of reproducible analytic workflows for large observational datasets.
• Training or experience in pharmacoepidemiology is a plus but is not required.
• Prior experience in HIV, diabetes, or cardiometabolic research is also a plus but is not required.