Data Analyst (Department of Radiology)

About the role

The Perelman School of Medicine at the University of Pennsylvania is the oldest and one of the finest medical schools in the United States. Penn is rich in tradition and heritage and at the same time consistently at the forefront of new developments and innovations in medical education and research.

Responsibilities

• Perform processing, organization, harmonization, and quality control of multimodal biomedical datasets, including imaging, electrophysiology, physiological, behavioral, clinical, and biospecimen-related data.
• Develop and maintain computational workflows and analytical pipelines using programming languages such as Python, and R.
• Conduct statistical analyses and data visualization to extract meaningful insights from high-dimensional and longitudinal datasets.
• Assist with multimodal data integration, database management, and data standardization across research studies.
• Collaborate with interdisciplinary teams including clinicians, engineers, data scientists, and research coordinators.
• Contribute to preparation of figures, reports, presentations, and manuscripts for scientific dissemination.
• Support development of reproducible and scalable research infrastructure for translational biomedical studies.

Qualifications

• Bachelor’s degree in Biomedical Engineering, Computer Science, Data Science, Statistics, Neuroscience, Mathematics, Bioinformatics, or related quantitative fields, and 2-3 years of experience or an equivalent combination of education and experience required.
• Strong programming experience in Python, R, or related computational environments.
• Experience working with complex biomedical or scientific datasets.
• Familiarity with statistical analysis methods and data visualization techniques.
• Excellent organizational skills and attention to detail.
• Strong written and verbal communication skills.

Preferred Qualifications

• Experience with multimodal biomedical data, including imaging, electrophysiology, physiological, wearable, behavioral, or clinical datasets.
• Familiarity with machine learning, AI, signal processing, or quantitative modeling approaches.
• Exposure to database systems, cloud/HPC computing environments, or large-scale data workflows.
• Experience with scientific software tools and reproducible computational pipelines.
• Interest in translational biomedical research and precision health applications.