Senior Research Programmer/Analyst Pharmacoepidemiology

Job Summary

The Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital and Harvard Medical School was created to facilitate a wide range of activities related to the use and outcomes of medications. Its mission is to bring together medicine, epidemiology, biostatistics, health services research, data science, and related disciplines to evaluate the benefits, risks, utilization, and costs of existing and new prescription medications and to develop methods to optimize medication use.

The Senior Research Programmer/Analyst will support this mission as part of a highly collaborative multidisciplinary research environment comprising approximately 30 Harvard Medical School faculty members and 80 research and administrative staff, including a team of experienced research programmers/analysts. The Division’s research makes extensive use of large and complex real-world healthcare data, including longitudinal health insurance claims and electronic health records, as well as data from surveys and randomized trials.

The Senior Research Programmer/Analyst will have the opportunity to work closely with epidemiologists, biostatisticians, clinicians, and other quantitative researchers to translate scientific questions into reproducible analytic datasets and analyses. The Division uses a range of programming languages and computational tools to work with large-scale, complex healthcare data, with SAS serving as a core programming language and R and Python used in complementary applications across our research portfolio. Because a substantial body of our established analytic infrastructure is built in SAS, advanced SAS expertise is essential for this position, while proficiency in R and/or Python is strongly valued.

Principal Duties and Responsibilities

• Support faculty and other research staff in the development of analytic datasets and the conduct of statistical analyses for pharmacoepidemiologic, health services, and related research.
• Develop, validate, manage, analyze, and document large and complex healthcare datasets, including longitudinal health insurance claims and electronic health record data.
• Translate research questions and study protocols into efficient and reproducible programming workflows, including data cleaning, transformation, linkage, cohort construction, variable creation, and statistical analysis.
• Apply programming best practices, including systematic quality control, code review, version control, modular and reusable programming, and clear documentation to support reproducibility and collaboration.
• Contribute to the Division’s programming and research-computing environment across SAS, R, Python, SQL, and related tools, including identifying opportunities to improve workflows, computational efficiency, and reusable infrastructure.
• Collaborate closely with faculty and research staff across multiple ongoing and new research projects.
• Participate in programmer/analyst team meetings, mentor junior programmers/analysts, and serve as a technical resource to other members of the research team.

Qualifications

• Bachelor’s degree in computer science, data science, statistics, biostatistics, epidemiology, biomedical informatics, mathematics, engineering, or another relevant quantitative field required.
• Master’s degree preferred.
• At least 3 years of relevant programming and data-analysis experience required; 5 or more years of experience in a healthcare, research, or similarly data-intensive environment preferred.
• Advanced proficiency in SAS required, including substantial experience with data management and analysis of large datasets.
• Proficiency in R and/or Python strongly preferred.
• Experience with SQL and relational databases strongly preferred.
• Experience working with large, complex, longitudinal datasets required; experience with healthcare claims and/or electronic health record data strongly preferred.

Skills / Abilities / Competencies Required

• Strong programming and problem-solving skills, with careful attention to accuracy, reproducibility, and quality control.
• Ability to manage and prioritize multiple concurrent research projects.
• Experience working in Linux/Unix-based command-line environments and remote computing systems.
• Experience with version-control systems such as Git/GitHub or similar platforms.
• Ability to work effectively in multidisciplinary research teams and communicate technical concepts clearly to both technical and non-technical collaborators.

Additional Desired Skills

• Experience developing reusable, efficient, and reproducible data-processing or analytic workflows for large datasets.
• Familiarity with high-performance, cloud-based, or distributed computing environments and modern software development practices.
• Knowledge of epidemiologic and biostatistical methods and/or experience with emerging computational approaches, including machine learning and AI-enabled research tools.