Biostatistician

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This is a 12-month, term-limited position contingent on continued grant funding. Renewal beyond the initial term is possible but not guaranteed. This role bridges traditional biostatistics and modern data science, handling everything from prospective power analyses and data architecture design through advanced longitudinal modeling, machine learning, and data sharing in open-science repositories. The ideal candidate is recognized as a subject-matter expert in quantitative methods, works independently with a high degree of autonomy, and is comfortable both executing complex analyses directly and reviewing/mentoring the work of less experienced analysts. Responsibilities* Study Planning & Methodological Design (20%) Perform sample size and power calculations, including simulation-based power analyses in R, for complex study designs prior to data collection. Design data management protocols, randomization strategies, and data architecture for clinical trials and observational cohorts. Complex Statistical & Data Science Analysis (45%) Conduct advanced statistical analyses across cross-sectional, prospective longitudinal, randomized controlled trial (RCT), and intensive longitudinal data (ILD/ecological momentary assessment/wearable or passive sensor) formats. (EF) Implement frequentist mixed-effects models and generalized estimating equations in R (lme4, nlme, glmmTMB). (EF) Model latent variables, structural equations, and longitudinal growth patterns in Mplus or R. Diagnose missing data mechanisms and apply modern handling methods (e.g., FIML, multiple imputation) suited to longitudinal attrition. (EF) Apply advanced hierarchical/multilevel models using both frequentist (lme4, glmmTMB) and Bayesian (Stan, brms, rstan) approaches as appropriate to the research question. (EF) Build and evaluate time-series machine learning and deep learning pipelines in Python (scikit-learn, PyTorch). Data Governance, Safety Monitoring & Open Science (20%) Prepare interim reports, safety metrics, and blinded/unblinded summaries for Data Safety and Monitoring and funding sponsors (e.g., NIH). (EF) Establish data management frameworks that ensure data integrity, HIPAA/IRB compliance, and reproducible analytic pipelines. Package, annotate, and document clean codebooks, datasets, and analysis scripts for deposit in public/restricted repositories (e.g., ICPSR, GitHub). (EF) Lead the development and team-wide adoption of best practices for study documentation, reproducible analytic workflows, and data management standards. (EF) Co-author peer-reviewed manuscripts, grant applications, and conference presentations, contributing statistical methods text, methodological descriptions, and publication-grade data visualizations. Review and provide feedback on analytic code and outputs produced by less experienced lab staff or trainees; serve as a technical resource on quantitative methods within the lab. (EF) Required Qualifications* Master's degree or Ph.D. in Quantitative Psychology, Biostatistics, Data Science, Applied Statistics, Information Science, or a related quantitative field, or an equivalent combination of education (with a minimum of a Bachelor's degree) and experience. 3+ years of post-degree experience supporting health, behavioral, or clinical research projects. Demonstrated hands-on proficiency in both R and Python for statistical computing and data science workflows. Experience with intensive longitudinal or ecological momentary assessment (EMA) data, including passive sensor/wearable data streams. Experience with reproducible research workflows, including version control (Git/GitHub) and dynamic reporting tools (Quarto, R Markdown, or Jupyter Notebooks). Demonstrated experience producing publication-grade data visualizations (e.g., ggplot2, matplotlib/seaborn, or similar) to communicate statistical results to scientific and non-specialist audiences. Experience with REDCap database management and automated API-based data ingestion from connected devices or third-party platforms. Excellent written and oral communication skills, including the ability to translate statistical methods for non-specialist audiences (e.g., manuscript co-authors, team members, sponsors). Proven ability to contribute to a positive workplace culture and demonstrate the school's core values. Desired Qualifications* Experience applying machine learning methods (e.g., scikit-learn, PyTorch) to time-series or behavioral data. Experience with latent growth curve modeling (e.g., Mplus, lavaan). Experience preparing DSMB reports or interim safety summaries for NIH-funded clinical trials. Experience curating and depositing research data/codebooks in open-science repositories (OSF, ICPSR, GitHub) and reporting results on clinicaltrials.gov. Familiarity with CONSORT/GCP trial design standards. Experience mentoring junior analysts, research assistants, or trainees in statistical programming, data management, and study documentation. Experience building interactive dashboards or reporting tools (e.g., Shiny, Quarto dashboards, Plotly) for sharing results with investigators or sponsors. Experience with dynamical systems modeling, system identification, or control-theoretic approaches to adaptive intervention design. Why Work at Michigan? In addition to a career filled with purpose and opportunity, The University of Michigan offers a comprehensive benefits package to help you stay well, protect yourself and your family and plan for a secure future. Benefits include: Generous time off A retirement plan that provides two-for-one matching contributions with immediate vesting Many choices for comprehensive health insurance Life insurance Long-term disability coverage Flexible spending accounts for healthcare and dependent care expenses Maternity and parental leave University commitment to providing reasonable accommodations to individuals with disabilities Modes of Work Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes . Additional Information This role sits at the intersection of behavioral motivation science and quantitative methods innovation within the University of Michigan's research community, working alongside a collaborative team committed to open science, methodological rigor, and translational impact. This position is available starting immediately and is term-limited, funded for 12 months; the initial appointment may be extended depending on the availability of research funds and successful performance. An offer may be extended outside the general salary range shared in this posting based on equity, experience, knowledge, and skills for this role. You are encouraged to discuss salary questions to honor understanding and transparency throughout the recruiting process. This is an onsite position, located at the School of Kinesiology Building on central U-M campus. The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third-party administrator to conduct background checks. Background checks will be performed in compliance with the Fair Credit Reporting Act. Application Deadline Job openings are posted for a minimum of seven calendar days.The review and selection process maybegin as early as the eighth day after posting.Thisopening may be removed from posting boards and filled any time after the minimum posting period has ended. U-M EEO Statement The University of Michigan is an Equal Opportunity Employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants, including protected veterans and individuals with disabilities. #J-18808-Ljbffr