Biostatisticians

Biostatisticians

The Study Design and Biostatistics Center (SDBC) at the University of Utah is seeking a highly motivated Master’s‑level biostatistician to join a team of approximately thirty biostatisticians and epidemiologists. The successful candidate will work in the Adam Bress Lab on high‑impact projects that apply cutting‑edge methods—including target trial emulation and modern causal inference—to evaluate the effectiveness, harms, and costs of antihypertensive treatments.

The lab uses both randomized trials and large‑scale electronic health record (EHR) data to study outcomes such as cardiovascular disease, dementia, and cancer, addressing real‑world evidence challenges such as treatment non‑adherence, fixed and time‑varying confounding, irregular assessment times, informative censoring, and unmeasured confounding. The Bress Lab also investigates treatment‑effect heterogeneity and benefit‑harm trade‑offs to generate evidence that informs clinical decision‑making and the design of confirmatory trials.

The role supports all phases of the research process, including study design, data management, statistical programming, analysis, visualization, and interpretation of results. Responsibilities include writing analysis plans, preparing reports and manuscripts, and clearly explaining statistical methods and findings to diverse collaborators. Candidates should demonstrate strong proficiency in R programming, experience managing and analyzing large‑scale datasets (e.g., EHR), and a commitment to producing accurate, reproducible code.

A strong interest in continuously learning new statistical methods, excellent communication skills, and the ability to work both independently and collaboratively are essential. This position offers opportunities to collaborate with NIH‑funded investigators on rigorous methodological research in comparative effectiveness, survival analysis, and causal inference, and includes mentorship by PhD‑level biostatisticians and ongoing professional development.

• Clean and manage large, complex datasets; develop reproducible code pipelines; implement quality assurance checks and maintain clear documentation.
• Write statistical analysis plans and perform sample size calculations; conduct data analyses; generate reports and graphical summaries; and contribute to presentations and publications.
• Maintain a solid foundation in statistical methods, including multivariable regression, longitudinal data analysis, categorical data analysis, and survival analysis.
• Conduct comparative effectiveness analysis using modern causal inference methods such as inverse probability weighting and related approaches.
• Implement target‑trial emulation framework using large‑scale observational data sources, including electronic health records.
• Write accurate, modular, and well‑documented R code with an emphasis on reproducibility and transparency.
• Collaborate effectively with investigators from diverse disciplines and communicate statistical results clearly to both technical and non‑technical audiences.

• Master’s degree in Biostatistics, Statistics, Data Science, or a related quantitative field.
• Solid knowledge of standard statistical analysis procedures, especially survival analysis and longitudinal data analysis.
• Proficiency in R programming, with demonstrated ability to write accurate, efficient, and well‑documented code; experience developing R packages or analytic pipelines is highly desirable.
• Experience working with large‑scale observational data, including data wrangling, cleaning, and harmonization.
• Familiarity with comparative effectiveness research and basic causal inference methods (e.g., counterfactual framework, inverse probability weighting).
• Excellent verbal and written communication skills, with the ability to explain technical concepts clearly to non‑statistical audiences.
• Demonstrated initiative and ownership of analytic tasks, with strong organizational skills and the ability to manage multiple projects simultaneously.
• Genuine enthusiasm for learning and applying new statistical methods in collaborative, interdisciplinary research settings.