Postdoctoral Associate, School of Public Health, Global Health

The Department of Global Health at the Boston University School of Public Health (BUSPH) is seeking a Postdoctoral Associate with strong training in epidemiology, causal inference, and the analysis of large healthcare datasets to join the research team of Dr. Alana T. Brennan, Associate Professor of Global Health and Epidemiology. The Postdoctoral Associate will play a central role in 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 leverages 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 multidisciplinary investigative team includes Co-Investigators Dr. Lawrence C. Long, Research Associate Professor of Global Health with expertise in health economics and applied health research, and Dr. Andrew C. Stokes, Associate Professor of Global Health with expertise in population health, epidemiologic methods, and causal inference. The R01 includes three complementary aims: characterizing real-world treatment patterns and early adverse events; estimating causal effects of GLP-1RAs versus other antidiabetes medications using target trial emulation; and evaluating long‑term clinical and economic outcomes. The Postdoctoral Associate will primarily contribute to the epidemiologic and causal inference components of the study, with opportunities to collaborate across all three aims. This position is particularly well suited for an early-career epidemiologist interested in applying modern epidemiologic and causal inference methods to complex, large-scale real‑world healthcare data. Prior experience in HIV, diabetes, or cardiometabolic research is not required. Required Skills Applicants should have: A doctoral degree (PhD, ScD, DrPH, or equivalent) in epidemiology or a closely related quantitative population health field by the anticipated start date. 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 with SQL for database management and querying large datasets is highly desirable. 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. Preferred Qualifications Particularly competitive applicants will have experience in one or more of the following: Target trial emulation, including new‑user, active‑comparator study designs. Advanced causal inference methods, including propensity score methods, inverse probability weighting, marginal structural models, and methods for time‑varying exposures and confounding. Analysis of large U.S. administrative medical and pharmacy claims data or similarly complex longitudinal healthcare datasets. SQL, database management, code optimization, parallelization, and/or other approaches for efficiently analyzing very large healthcare datasets. Survival and time‑to‑event analysis, including competing‑risk methods. Causal mediation analysis. 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. We are particularly interested in candidates with strong epidemiologic methods and causal inference training who can apply these skills to complex, large‑scale real‑world healthcare data. Required Experience Qualifications: Applicants should have: A doctoral degree (PhD, ScD, DrPH, or equivalent) in epidemiology or a closely related quantitative population health field by the anticipated start date. 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 with SQL for database management and querying large datasets is highly desirable. 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. Preferred Qualifications Particularly competitive applicants will have experience in one or more of the following: Target trial emulation, including new‑user, active‑comparator study designs. Advanced causal inference methods, including propensity score methods, inverse probability weighting, marginal structural models, and methods for time‑varying exposures and confounding. Analysis of large U.S. administrative medical and pharmacy claims data or similarly complex longitudinal healthcare datasets. SQL, database management, code optimization, parallelization, and/or other approaches for efficiently analyzing very large healthcare datasets. Survival and time‑to‑event analysis, including competing‑risk methods. Causal mediation analysis. 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. We are particularly interested in candidates with strong epidemiologic methods and causal inference training who can apply these skills to complex, large‑scale real‑world healthcare data. _We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, natural or protective hairstyle, religion, sex, age, national origin, physical or mental disability, sexual orientation, gender identity, genetic information, military service, pregnancy or pregnancy‑related condition, or because of marital, parental, or veteran status. We are a VEVRAA Federal Contractor. _ #J-18808-Ljbffr Boston University