Statistician Intermediate

Chronic Pain and Fatigue Research Center (CPFRC)

The Chronic Pain and Fatigue Research Center (CPFRC) at the University of Michigan is a pioneering interdisciplinary center dedicated to advancing our understanding of chronic pain and fatigue disorders. Since our founding in 1998, we’ve been leaders in identifying how distinct conditions—such as fibromyalgia, interstitial cystitis, osteoarthritis, low back pain, and post-deployment syndromes—often share common biological mechanisms.

What we offer:

• A collaborative, intellectually stimulating environment with mentorship from experienced faculty biostatisticians.
• Exposure to a rich and diverse research portfolio spanning NIH-funded multi-center trials, longitudinal cohorts, and translational studies.
• Strong opportunities for career development, skill growth in advanced statistical methods, and contributions to peer-reviewed publications.
• Hybrid work schedule with flexibility; full-time, regular appointment.
• Access to the University of Michigan’s world-class research infrastructure, training programs, and academic community.

Conduct statistical analyses across a range of study designs and data types, following Statistical Analysis Plans (SAPs) developed by the Faculty Statistician, including:

• Longitudinal & repeated-measures studies (e.g., linear/nonlinear mixed-effects models, GEE, repeated-measures ANOVA)
• Survival & time-to-event analyses (e.g., Cox proportional hazards, Kaplan-Meier, competing risks, accelerated failure time models)
• Observational & cross-sectional studies (e.g., logistic regression, ordinal regression, GLMs, propensity score methods)
• Dimension reduction & exploratory analyses (e.g., PCA, factor analysis, latent class analysis)

Perform model diagnostics, sensitivity analyses, and QA checks on all delivered analyses; communicate findings and any analytical concerns proactively to the Faculty Statistician.

Develop and execute well-documented, reproducible analysis code in R and/or SAS, using platforms such as Quarto or R Markdown for reproducible reporting.

Produce publication-quality tables, figures, and data summaries for manuscripts, conference presentations, and sponsor reports; address statistical reviewer comments on manuscripts in collaboration with the Faculty Statistician and investigators.

Collaborate with Data Managers to clean, validate, and transform raw datasets into analysis-ready analytic files aligned with SAP specifications.

Perform data quality checks, evaluate data validity, and document all data preparation steps, input/output file locations, and version histories.

Write programs for data acquisition, preparation, and analytic file creation across multiple concurrent studies (single-center and multi-center).

Contribute to the statistical methods and results sections of research manuscripts; support the peer-review and revision process.

Provide statistical input for grant proposals, including assistance with power and sample size calculations, analysis plan narrative, and analytical approach sections—under the guidance of the Faculty Statistician.

Participate in research presentations at internal meetings, conferences, and workshops as appropriate.

Serve as an active member of project teams comprising faculty investigators, data managers, study coordinators, and research trainees; contribute to weekly triage meetings and project stand-ups.

Communicate clearly and proactively with the Faculty Statistician and Principal Investigators about analytical progress, results, and any data or methodological issues.

Uphold CPFRC standards for data governance, documentation, and reproducibility.

• Bachelor's degree with 1-3 years of professional experience
• Previous coding and data analysis experience, including relevant research project experience gained during training.
• Strong theoretical and applied foundation in statistical methods, including linear and nonlinear regression, generalized linear models, mixed-effects models, and survival analysis, etc.
• Proficiency in R and/or SAS for data management,…