More jobs:
Data Scientist IV
Job in
Pittsburgh, Allegheny County, Pennsylvania, 15289, USA
Listed on 2026-06-03
Listing for:
University of Pittsburgh
Full Time
position Listed on 2026-06-03
Job specializations:
-
IT/Tech
Data Scientist, Data Analyst, Data Science Manager -
Research/Development
Data Scientist
Job Description & How to Apply Below
Provides subject matter expertise in data manipulation, statistical data methodologies, data analysis, and development of new data science methods. Collaborates with principal investigators or study sponsors on high-level study design, data collection, data set analysis, protocol development, statistical analysis and inference, and report writing and presentations for internal and external audience. Leads development of complex analytical plans, creates systems for data monitoring, provides high-level statistical support, utilizes statistical software, creates research study reports, and contributes to manuscripts, grants, and scientific presentations.
Adheres to all protocols.
Physical Effort
Lead the management, integration, and quality assurance of large-scale analytic datasets, collaborating with data managers at partner institutions to ensure consistency, harmonization, and reproducibility across sites.
Design and implement sophisticated statistical analyses, including multilevel/hierarchical modeling, longitudinal analyses, and spatial methodologies to address complex research questions.
Contribute to and help drive peer-reviewed publications, including drafting and refining statistical analysis sections and ensuring methodological rigor.
Partner closely with investigators, faculty, and trainees to develop analytic strategies, refine research questions, and guide the execution and interpretation of analyses.
Apply advanced SAS programming skills, with experience or interest in working with neighborhood-level, geospatial, and census-linked data.
Produce high-quality, publication-ready figures and tables using tools such as ggplot2, matplotlib, Tableau, or similar visualization platforms.
Translate complex analytical outputs into clear, compelling visualizations and summaries, making findings accessible to non-technical stakeholders.
Develop or support the development of interactive data products and dashboards (e.g., R Shiny, Quarto, or similar tools) to enhance dissemination and stakeholder engagement.
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