University of Utah Health Research
Biostatisticians
University of Utah Health Research, Salt Lake City, Utah, United States, 84193
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Biostatisticians
role at
University of Utah Health Research
Open Date: 12/02/2025 | Close Date: 03/02/2026 | Pay Rate Range: $47,600 - $90,400 | Full‑time, Day Shift, Hybrid or Fully Remote (subject to approval) | Location: Salt Lake City, UT (Campus)
Job Summary The Study Design and Biostatistics Center (SDBC) at the University of Utah seeks a highly motivated Master’s‑level biostatistician to join a team of approximately 30 biostatisticians and epidemiologists. The candidate will work in the Adam Bress Lab on high‑impact projects using target‑trial emulation and modern causal inference to evaluate the effectiveness, harms, and costs of antihypertensive treatments. The lab utilizes randomized trials and large‑scale electronic health record (EHR) data for outcomes such as cardiovascular disease, dementia, and cancer. Responsibilities span study design, data management, statistical programming, analysis, visualization, and interpretation of results. Candidates should demonstrate strong proficiency in R programming, experience with large‑scale datasets (e.g., EHR), and a commitment to producing accurate, reproducible code. This position offers collaboration with NIH‑funded investigators and mentorship by PhD‑level biostatisticians.
Responsibilities
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; 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 frameworks 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.
Minimum Qualifications
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 a 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.
Familiar 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.
Preferences
Experience applying target‑trial emulation frameworks using observational data.
Experience using Python for statistical programming and data science applications.
Experience using AI‑assisted tools (e.g., ChatGPT, GitHub Copilot) to enhance productivity and code quality.
Strong organizational skills with the ability to manage competing priorities and meet project deadlines.
Demonstrated ability to work effectively in a collaborative, interdisciplinary research team.
Prior experience in biomedical or clinical research is desirable but not required.
At least one year of experience in a statistical consulting or statistical programming role.
Meticulous attention to code accuracy, reproducibility, and documentation; experience with version control systems (e.g., Git) is a plus.
Self‑motivated and committed to producing high‑quality, impactful research in a collaborative setting.
Equal Opportunity / EEO Statement The University of Utah values candidates who have experience working in settings with students and possess a strong commitment to improving access to higher education.
Veterans’ preference is extended to qualified applicants, upon request and consistent with University policy and Utah state law. Upon request, reasonable accommodations in the application process will be provided to individuals with disabilities.
Consistent with state and federal law, the University of Utah does not discriminate based upon race, ethnicity, color, religion, national origin, age, disability, sex, sexual orientation, gender, gender identity, gender expression, pregnancy, pregnancy‑related conditions, genetic information, or protected veteran’s status. The University does not discriminate on the basis of sex in the education program or activity that it operates, as required by Title IX and 34 CFR part 106. The requirement not to discriminate in education programs or activities extends to admission and employment. Inquiries about the application of Title IX and its regulations may be referred to the Title IX Coordinator, to the Department of Education, Office for Civil Rights, or both.
To request a reasonable accommodation for a disability or if you or someone you know has experienced discrimination or sexual misconduct including sexual harassment, you may contact the Director/Title IX Coordinator in the Office of Equal Opportunity and Title IX (OEO). More information, including the Director/Title IX Coordinator’s office address, electronic mail address, and telephone number can be located at: https://www.utah.edu/nondiscrimination/.
Online reports may be submitted at
oeo.utah.edu . See
https://safety.utah.edu/safetyreport
for additional reporting resources. A paper copy can be obtained by request at the Department of Public Safety located at 1658 East 500 South.
#J-18808-Ljbffr
Biostatisticians
role at
University of Utah Health Research
Open Date: 12/02/2025 | Close Date: 03/02/2026 | Pay Rate Range: $47,600 - $90,400 | Full‑time, Day Shift, Hybrid or Fully Remote (subject to approval) | Location: Salt Lake City, UT (Campus)
Job Summary The Study Design and Biostatistics Center (SDBC) at the University of Utah seeks a highly motivated Master’s‑level biostatistician to join a team of approximately 30 biostatisticians and epidemiologists. The candidate will work in the Adam Bress Lab on high‑impact projects using target‑trial emulation and modern causal inference to evaluate the effectiveness, harms, and costs of antihypertensive treatments. The lab utilizes randomized trials and large‑scale electronic health record (EHR) data for outcomes such as cardiovascular disease, dementia, and cancer. Responsibilities span study design, data management, statistical programming, analysis, visualization, and interpretation of results. Candidates should demonstrate strong proficiency in R programming, experience with large‑scale datasets (e.g., EHR), and a commitment to producing accurate, reproducible code. This position offers collaboration with NIH‑funded investigators and mentorship by PhD‑level biostatisticians.
Responsibilities
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; 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 frameworks 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.
Minimum Qualifications
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 a 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.
Familiar 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.
Preferences
Experience applying target‑trial emulation frameworks using observational data.
Experience using Python for statistical programming and data science applications.
Experience using AI‑assisted tools (e.g., ChatGPT, GitHub Copilot) to enhance productivity and code quality.
Strong organizational skills with the ability to manage competing priorities and meet project deadlines.
Demonstrated ability to work effectively in a collaborative, interdisciplinary research team.
Prior experience in biomedical or clinical research is desirable but not required.
At least one year of experience in a statistical consulting or statistical programming role.
Meticulous attention to code accuracy, reproducibility, and documentation; experience with version control systems (e.g., Git) is a plus.
Self‑motivated and committed to producing high‑quality, impactful research in a collaborative setting.
Equal Opportunity / EEO Statement The University of Utah values candidates who have experience working in settings with students and possess a strong commitment to improving access to higher education.
Veterans’ preference is extended to qualified applicants, upon request and consistent with University policy and Utah state law. Upon request, reasonable accommodations in the application process will be provided to individuals with disabilities.
Consistent with state and federal law, the University of Utah does not discriminate based upon race, ethnicity, color, religion, national origin, age, disability, sex, sexual orientation, gender, gender identity, gender expression, pregnancy, pregnancy‑related conditions, genetic information, or protected veteran’s status. The University does not discriminate on the basis of sex in the education program or activity that it operates, as required by Title IX and 34 CFR part 106. The requirement not to discriminate in education programs or activities extends to admission and employment. Inquiries about the application of Title IX and its regulations may be referred to the Title IX Coordinator, to the Department of Education, Office for Civil Rights, or both.
To request a reasonable accommodation for a disability or if you or someone you know has experienced discrimination or sexual misconduct including sexual harassment, you may contact the Director/Title IX Coordinator in the Office of Equal Opportunity and Title IX (OEO). More information, including the Director/Title IX Coordinator’s office address, electronic mail address, and telephone number can be located at: https://www.utah.edu/nondiscrimination/.
Online reports may be submitted at
oeo.utah.edu . See
https://safety.utah.edu/safetyreport
for additional reporting resources. A paper copy can be obtained by request at the Department of Public Safety located at 1658 East 500 South.
#J-18808-Ljbffr