Data Scientist II - Quantitative Biology, Multi-OMICs Integration, and AI/ML Modeling

The Pulmonary, Allergy, Critical Care, and Sleep Medicine Division is made of up approximately 70 clinical staff and over 100 research staff who work in a variety of locations including: Bridgeside Point, Biomedical Science Tower (BST), UPMC Hospitals including Presbyterian, Shadyside, East, Jameson and Mercy. The division provides world class services across 10 inpatient services at five hospitals and over 70,000 outpatient office visits.

We have a major research mission that focuses on basic, clinical, and translational research on the lung, microbiome, COVID-19, sleep medicine, and allergy and immunology. The Benam Lab ( at the University of Pittsburgh Departments of Medicine and Bioengineering is seeking a highly motivated Data Scientist II to provide advanced analytical and computational expertise for a multidisciplinary research program focused on human-relevant experimental systems (microphysiological systems; Organs-on-Chips).

This role will lead the development and implementation of data analysis pipelines, statistical modeling frameworks, and machine learning approaches to enable integration and interpretation of complex biological datasets. The position will support high-dimensional data analysis across virology, imaging, and multi-OMICs platforms, and will contribute to development of standardized, regulatory-aligned data frameworks to support predictive modeling and translational decision-making. Required Qualifications
Master's degree (Ph.

D. preferred) in Bioinformatics, Biostatistics, Computational Biology, Data Science, or a related quantitative field
Minimum of 3 years of relevant experience in data analysis, statistical modeling, or computational biology (or equivalent combination of education and experience)
Strong expertise in statistical analysis, quantitative modeling, and data interpretation
Proficiency in programming languages commonly used in scientific computing (e.g., Python, R)
Demonstrated experience working with complex, high-dimensional biological datasets
Experience developing reproducible computational workflows and data processing pipelines
Strong analytical, problem-solving, and communication skills
Ability to work independently and collaboratively in multidisciplinary environments

Preferred Qualifications
Ph. D. in a relevant quantitative or computational discipline
Experience with multi-OMICs data integration (e.g., transcriptomics, proteomics, metabolomics)
Experience with machine learning, deep learning, or data-driven modeling approaches in biomedical research
Familiarity with image analysis workflows and quantitative imaging data
Experience with systems biology approaches and integrative modeling
Experience developing or supporting regulatory-aligned data frameworks or standardized data pipelines
Experience contributing to scientific publications, grant proposals, or regulatory submissions
Experience working in multidisciplinary teams integrating computational and experimental research