Biostatistician-Intermediate

The Population Neuroscience Core (PNC) within the Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases at The University of Texas Health Science Center at San Antonio (UT Health San Antonio) is seeking an exceptional Biostatistician to join a dynamic, collaborative research team conducting cutting-edge research on brain aging and Alzheimer’s disease. This position is particularly well suited for a biostatistician or data analyst who enjoys working collaboratively with investigators and applying statistical methods to complex, real-world epidemiologic data.

The Biostatistician will provide statistical and data-analytic support across the Core’s portfolio of epidemiologic and population-based research programs. The position will work closely with faculty investigators, epidemiologists, clinicians, research staff, and trainees to design studies, develop and implement statistical analyses, manage and harmonize complex research datasets, interpret findings, and contribute to scientific publications and grant applications.

The PNC supports large-scale longitudinal cohort studies and collaborative research initiatives, including the San Antonio Heart and Mind Study (SAHMS), the South Texas Alzheimer’s Disease Research Center, MarkVCID, the Cross-Cohorts Consortium, and other national and international consortia. Our research datasets are richly phenotyped and may include longitudinal clinical and cognitive assessments, psychosocial measures, neuroimaging, biomarkers, genomic and other omics data, and wearable/sensor data.

• Performs statistical analyses for epidemiologic research studies and selects appropriate statistical methods based on study design, with particular emphasis on longitudinal and repeated-measures data.

• Cleans, manages, and analyzes complex research datasets; performs data quality control and validation and maintains documentation, data dictionaries, and analysis-ready datasets.

• Assists with sample size and power calculations

• Supports harmonization of variables, measures, and analytical approaches across multiple studies and cohorts.

• Develops statistical programs, tables, charts, and graphs to communicate research findings; contributes to manuscripts, abstracts, presentations, and grant applications.

• Maintains clear documentation of data processing, analytical datasets, statistical programming, and quality-control procedures.

• Collaborates with investigators, data coordinators, and research staff to support high-quality, reproducible analyses across diverse datasets, including clinical, cognitive, neuroimaging, biomarker, omics, and wearable data.

• Maintains statistical software and computing resources used for research analyses.

• Assists investigators with preparation of statistical methods and results for scientific publications, abstracts, presentations, research reports, and grant applications.

• Performs all other duties as assigned.

• The successful candidate will have strong knowledge of statistical methods and their application to epidemiologic research, including longitudinal and repeated-measures data. Proficiency in R and statistical programming, with demonstrated ability to clean, manipulate, analyze, and visualize research data, is expected. Strong attention to detail and the ability to develop well-documented, reproducible analytical workflows are essential.

• The candidate should have strong written and verbal communication skills, including the ability to explain statistical concepts and research findings clearly to investigators and other non-statistical collaborators. Strong organizational, interpersonal, and collaborative skills are essential, as is the ability to manage multiple projects and priorities and work effectively as part of a multidisciplinary research team.

Education:

• A Master’s degree in biostatistics, statistics, epidemiology, or another recognized field of science or learning directly related to the duties of the position is required.

Experience:

• Three (3) years of experience is required.

Preferred Experience:

• Clinical, epidemiologic, or population-based research methods and study design.
• Statistical analysis of research data.
• Statistical programming and demonstrated ability to perform analyses using statistical software, preferably R.
• Data manipulation, cleaning, quality control, and preparation of analysis-ready datasets.
• Statistical methods appropriate for longitudinal or repeated-measures data.

• Experience with causal inference methods is desirable.

• Experience working with large longitudinal cohort studies or complex biomedical datasets is highly desirable.

• Experience with neuroimaging, biomarkers, genomics/omics, wearable or sensor data, or multisite studies is a plus.