Biostatistician I at The University of Texas at Austin in Austin, TX

  • Company: The University of Texas at Austin
  • Location: Austin, TX
  • Job type: full time
  • Workplace: onsite
  • Posted: 2026-10-13

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Job description

Job Posting Title: Biostatistician I ---- Hiring Department: Department of Medicine ---- Position Open To: All Applicants ---- Weekly Scheduled Hours: 40 ---- FLSA Status: Exempt from FLSA ---- Earliest Start Date: Immediately ---- Position Duration: Expected to Continue Until Aug 31, 2029 ---- Location: AUSTIN, TX ---- Job Details: General Notes Dell Medical School is seeking a Bisostatistician I This is a grant-funded position with funding currently anticipated through August 31, 2029. Purpose The Biostatistician I supports translational cancer resesearch within the Department of Medicine’s Division of Oncology by performing statisticcal analyses, managing clinical and molecular datasets, and contributing to computational workflows under the supervision of senior statisticiansd and research faculty. This role assist with study design, data integration, reproducibility, and interpretation of results for projects involving molecular, genomic, and clinical cancer datasets. Responsibilities Designs and Implements Statistical Studies Assists in developing statistical analysis plans for translational cancer research studies. Supports sample size estimations, methodology selection, and workflow design for molecular and clinical datasets. Applies standard statistical techniques to multi-omnics, genomic, and clinical outcome data. Ensures statistical approaches align with regulatory and research rigor expectations. Analyzes and Interprets Data Performs statistical analyses of cancer genomics, molecular profiling, and clinical datasets using R, Python, SAS, or similar tools. Validates data integrity and reproduces results using documented analytic workflows. Interprets analytic outputs with guidance from senior statisticians and faculty. Prepares statistical summaries, tables, and figures of manuscripts, grants, and reports. Collaborates with Cross-Functional Teams Works with faculty, clinicians, and computational researchers to interpret statistical results and refine analyses. Participates in study meetings and contributes to data-driven decision-making. Supports protocol development, data requests, and integration of molecular and clinical datasets. Manages Data Systems and Tools Maintains, cleans, and curates research databases containing molecular, genomic, and clinical data. Creates reproducible workflows and basic data visualizations. Integrates datasets from public domain sources, institutional repositories, and structured internal pipelines. Documents all data preparation steps to ensure scientific rigor. Supports Regulatory and Ethical Compliance Follows HIPAA, IRB protocols, and institutional guidelines for human subjects research and genomic data handling. Ensures secure and ethnical management of patient-derived molecular data. Participates in audits or compliance reviews when required. Contributes to Scientific Communication Develops tables, figures, and code-generated outputs for manuscripts, conference presentations, progress reports, and grant applications. Supports drafting components of statistical or computational methods section. Other related duties as assigned. MARGINAL OR PERIODIC FUNCTIONS: Adheres to internal controls, reporting structure, and departmental requirements. Performs other related duties as assigned. KNOWLEDGE/SKILLS/ABILITIES Analytical Skills Able to analyze large, complex genomic, molecular, and clinical datasets and derive meaningful insights. Skilled: Analyzes complex data sets to derive meaningful insights. Technical Expertise Proficient in R, Python, SAS, SQL, or comparable tools for statistical and computational analysis. Familiar with data visualization, reproducible research practices, and version control tools (e.g., Git). Collaboration Works effectively with interdisciplinary scientific and clinical teams. Attention to Detail Ensures accuracy and reproducibility in data processing, statsitical modeling, and documentation. Problem Solving Idenitifies statistical challenges, recommends s

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