Job description

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 (benamlab.net) 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 Performs research, analysis, and modeling on organizational data and data sets utilizing advanced statistical methods to mine and analyze complex and unstructured data sets. Offers complex analysis and utilizes advanced algorithms and statistical software. Adheres to all protocols. • Design and implement robust, scalable data analysis pipelines for multimodal datasets, including virology, imaging, and multi-OMICs data • Lead development and application of statistical models to quantify infection dynamics and therapeutic response endpoints • Develop and apply machine learning and AI-based approaches for pattern recognition, predictive modeling, and target discovery • Perform integrative, systems-level analyses across diverse data types (molecular, cellular, functional, and temporal datasets) • Collaborate with investigators on study design, statistical planning, and development of quantitative frameworks for experimental studies • Establish and maintain reproducible computational workflows, including data preprocessing, quality control, and version-controlled pipelines • Develop data standards, organization strategies, and reporting structures aligned with regulatory expectations (e.g., ISTAND-ready frameworks) • Generate high-quality data visualizations, analytical reports, and summaries to support internal decision-making and external dissemination • Contribute to preparation of manuscripts, grant applications, and technical reports • Support development and maintenance of data infrastructure to enable efficient data storage, access, and analysis • Collaborate closely with multidisciplinary teams spanning biology, engineering, and computational sciences Must be able to work on a computer for prolonged periods of time. The University of Pittsburgh is an equal opportunity employer / disability / veteran.

Requirements

Must-haves

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

Nice-to-haves

Master’s degree (Ph.D. preferred) in Bioinformatics, Biostatistics, Computational Biology, Data Science, or a related quantitative field
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
Design and implement robust, scalable data analysis pipelines for multimodal datasets, including virology, imaging, and multi-OMICs data
Lead development and application of statistical models to quantify infection dynamics and therapeutic response endpoints
Develop and apply machine learning and AI-based approaches for pattern recognition, predictive modeling, and target discovery

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machine learningpythonai

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