Biostatistician I

Job Summary

We are seeking a skilled and detail-oriented individual to join our team as a Part-Time Data Analyst for the National Heart, Lung and Blood Institute (NHLBI) Advancing Research in Cardiovascular Health and Endometriosis Study (ARCHES). The Data Analyst, under guidance of team Principle and Co-Investigators, will be responsible for analyzing complex health-related data, with a focus on longitudinal data analysis, population databases, and causal inference.

Responsibilities

• Data Cleaning and Preparation: Clean, validate, and prepare datasets for analysis, ensuring data quality and integrity. Handle missing data, outliers, and inconsistencies appropriately.
• Statistical Modeling: Develop and apply appropriate statistical models to analyze complex health data, including regression models, survival analysis, hierarchical models, mediation analyses, validation work, and other relevant techniques with guidance from team Principal and Co-Investigators.
• Longitudinal Data Analysis: With guidance from team Principal and Co-Investigators, conduct longitudinal data analysis using appropriate methods such as mixed-effects models, test for effect modification, and conduct formal mediation analyses.
• Population Databases: Utilize population-level databases, such as electronic health records or Utah Population Database, to extract relevant information and perform statistical analysis.
• Causal Inference: Apply causal inference methods to evaluate confounding in observational studies.
• Data Visualization: Generate clear and informative data visualizations, graphs, and charts to present analytical findings and facilitate understanding by both researchers and the lay public.
• Quality Assurance: Ensure accuracy and reliability of analysis results by conducting rigorous quality assurance checks and validating analytical outputs.
• Collaboration and Communication: Collaborate effectively with interdisciplinary teams, including researchers, clinicians, and other data analysts. Clearly communicate analysis methods, results, and implications to both technical and non-technical audiences.
• Machine Learning (Plus): Utilize machine learning techniques to analyze health outcome data, develop predictive models, and identify patterns and trends in large datasets.
• Work Environment and Level of Frequency typically required: Nearly Continuously: Office environment.
• Physical Requirements and Level of Frequency that may be required: Nearly Continuously: Sitting, hearing, listening, talking. Often: Repetitive hand motion (such as typing), walking. Seldom: Bending, reaching overhead.

Minimum Qualifications

This position requires familiarity with standard statistical analysis procedures with a minimum of a BS degree in statistics, biostatistics, a related field, or equivalency (one year of education can be substituted for two years of related work experience); General programming skills or familiarity with at least one statistical programming language such as SAS or R with the ability to independently gain new skills and solve difficult programming challenges; Experience with Microsoft Office (Word, Excel, PowerPoint) is necessary; An ability to work on several projects simultaneously and manage deadlines, and good communication skills.

Preferences

• Education: Bachelor's degree in a relevant field (e.g., statistics, epidemiology, biostatistics, public health) or equivalent experience. Master's degree is a plus.
• Experience: Minimum of 2-3 years of experience in statistical data analysis, preferably in a health sciences or healthcare setting.
• Longitudinal Data Analysis: Strong understanding and practical experience with longitudinal data analysis methods, including mixed-effects models.
• Population Databases: Familiarity with large-scale population-level databases, such as electronic health records, Utah Population Database, or administrative claims databases.
• Causal Inference: Knowledge of causal inference methods (e.g., directed acyclic graphs), mediation analyses, and related techniques to address confounding factors in observational studies and assessment of direct and indirect effects.
• Machine Learning (Plus): Experience with machine learning techniques and tools for health outcome research, including classification, regression, clustering, and deep learning algorithms.
• Statistical Software: Proficiency in statistical software such as SAS, Stata, R or Python, including relevant packages for data manipulation, visualization, and statistical modeling.
• Data Management: Strong data management skills, including data cleaning, merging, and transformation.
• Analytical Thinking: Strong analytical and problem-solving skills, with the ability to approach complex research questions and data challenges.
• Communication Skills: Excellent written and verbal communication skills, with the ability to convey technical concepts to both technical and non-technical stakeholders.
• Time Management: Ability to manage multiple projects, prioritize tasks, and meet deadlines in a fast-paced environment.

Special Instructions

Requisition Number: PRN16363N
Full Time or Part Time? Part Time
Work Schedule Summary: Monday - Friday from 9am to 1pm.
Department: 00958 - DFPM-Administration
Location: Campus
Pay Rate Range: $39,000 to $56,000 DOQ
Close Date: 12/8/2025
Open Until Filled:

To apply, visit https://utah.peopleadmin.com/postings/188639