Visiting Research Data Scientist - Illinois Fire Service Institute

About the role

This position offers the opportunity to contribute to high-impact occupational and environmental health research with the potential to improve firefighter health, inform exposure prevention strategies, and advance cancer prevention efforts through rigorous, data-driven science.

Responsibilities

• Data Management and Integration
• Integrate, manage, and curate complex, multi-source datasets, including survey data, occupational and environmental exposure data, physiological measurements, biomarker and laboratory assay data, and clinical and longitudinal research data.
• Develop and maintain secure, organized, and scalable data infrastructure for longitudinal research studies.
• Design and maintain reproducible data pipelines, workflows, and comprehensive documentation.
• Develop and maintain data dictionaries, codebooks, metadata documentation, and standard operating procedures.
• Perform data cleaning, validation, quality control, and auditing procedures to ensure data integrity and consistency.
• Career development and implementation of reproducible analytical workflows using version control and best practices in computational research.
• Statistical & Computational Analysis
• Perform statistical analyses of longitudinal, repeated-measures, and complex observational datasets.
• Apply advanced statistical, multivariate, and machine learning methods, including regression modeling, mixed-effects models, clustering, dimensionality reduction, predictive modeling, and classification algorithms.
• Identify, model, and interpret relationships between occupational or environmental exposures and biological responses.
• Address confounding, bias, and missing data using appropriate analytical approaches, sensitivity analyses, and model diagnostics.
• Develop analytic strategies for biomarker, epidemiologic, and translational research studies.
• Generate high-quality statistical summaries, figures, tables, and visualizations for scientific publications, presentations, and reports.
• Aid investigators with interpretation and communication of analytical findings.
• Collaboration & Research Support
• Collaborate closely with investigators and multidisciplinary research teams to translate scientific questions into rigorous analytical plans.
• Support preparation of manuscripts, conference abstracts, technical reports, and peer-reviewed publications.
• Contribute to grant proposals through preliminary analyses, data visualization, and methodological input.
• Coordinate with laboratory personnel, statisticians, clinicians, and external collaborators regarding data transfer, formatting, and harmonization.
• Ensure compliance with IRB requirements, HIPAA regulations, data-use agreements, institutional data governance policies, and human-subjects research protections.

Qualifications

• Master’s degree or PhD in Data Science, Biostatistics, Bioinformatics, Epidemiology, Computational Biology, Statistics, or a related quantitative field.
• Experience handling and managing sensitive and confidential human-subjects data.
• Proficiency in statistical programming languages such as R, Python, SAS, and SQL.
• Experience analyzing longitudinal, repeated-measures, or large-scale observational datasets.
• Experience developing clear data visualizations for technical and non-technical audiences.
• Strong foundation in statistical modeling and inference.
• Proficiency with REDCap database management and data quality workflows.
• Ability to communicate effectively with a wide range of audiences.
• Technical Skills: R/RStudio, Python (Pandas, NumPy, SciPy, scikit-learn), SQL databases, REDCap, Tableau or Power BI, Git/GitHub, Statistical modeling and data visualization packages.

Preferred Qualifications

• Experience working with biomarker, clinical, or environmental exposure data.
• Knowledge of methods used to address confounding, bias, and missing data in observational studies.
• Experience with machine learning applications in health research.
• Experience integrating heterogeneous data sources across research platforms or collaborating institutions.
• Proficiency with SQL, database systems, and/or data engineering tools.
• Experience implementing reproducible research practices using Git or other version control systems.
• Knowledge, Skills, and Abilities: Strong organizational skills and attention to detail. Ability to work independently and collaboratively in a multidisciplinary research environment.