Biostatisticians

Details

Open Date 12/02/2025

Requisition Number PRN
43715B

Job Title Biostatisticians

Working Title Biostatisticians

Career Progression Track P00

Track Level P3 - Career, P2 - Developing

FLSA Code Professional

Patient Sensitive Job Code? No

Standard Hours per Week 40

Full Time or Part Time? Full Time

Shift Day

Work Schedule Summary Employees must reside in the state of Utah but might have the opportunity to work a hybrid (in-office and remote) work schedule. Possible consideration of a request for a fully remote work schedule, subject to approval and change.

VP Area U of U Health - Academics

Department 00952 - Division of Epidemiology

Location Campus

City Salt Lake City, UT

Type of Recruitment External Posting

Pay Rate Range 47,600 to 90,400

Close Date 03/02/2026

Priority Review Date (Note - Posting may close at any time) 12/02/2025

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 30 biostatisticians and epidemiologists to collaborate with clinical and translational researchers in biomedical research. The successful candidate will work effectively in the Adam Bress Lab on a variety of 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.

The lab applies advanced causal inference methods to address key challenges in real‑world evidence research, including treatment nonadherence, fixed and time‑varying confounding bias, irregular assessment times, informative censoring, and unmeasured confounding. The Bress Lab also investigates treatment effect heterogeneity and benefit‑harm tradeoffs to generate high‑quality evidence that informs clinical decision‑making and the design of confirmatory trials.

The successful candidate will contribute to 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, combined with excellent communication skills and the ability to work both independently and collaboratively, is essential.

This position offers the opportunity to collaborate with NIH‑funded investigators on rigorous methodological research in comparative effectiveness, survival analysis, and causal inference. The role includes mentorship by PhD‑level biostatisticians and ongoing opportunities for professional development in a supportive academic environment.

Learn more about the great benefits of working for University of Utah: benefits.utah.edu

The department may choose to hire at any of the below job levels and associated pay rates based on their business need and budget.

• 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.
• Have 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…