The School of Data Science at the University of Virginia (UVA) is assembling a world-class faculty with broad expertise in data science to foster and advance our research agenda and educational programs. We are searching to fill multiple open rank tenured or tenure-track professorships in data science. At the School of Data Science, tenured and tenure-track faculty develop internationally recognized research programs, successfully compete for external research funding, instruct classes (in-person and online), and advise and mentor undergraduate and graduate students. We seek candidates with an innovative spirit who have strong skills in data science research, pedagogy, and advising. Faculty will have the unique opportunity to shape the culture and direction of both a new discipline and a new school.

Our curriculum is organized around the data science domains (listed below), and ideal applicants can teach in one or more of these domains.

• Data Systems (e.g., high-performance computing, continuous integration and deployment (CI/CD) of data science tools, cloud architectures, federated learning, data sharing)


• Data Design (e.g., visualization, human-computer interaction, communication)


• Data Ethics, Critical Data Studies & Policy (e.g., representativeness, privacy, ethics of algorithmic construction, interpretability)


• Machine Learning and Analytics (e.g., predictive modeling, algorithm development, statistical methods, graph theory)

The school is open to all areas of research and scholarship in data science. Presently, the school is especially looking to grow in the following areas.

• Generative AI, large language models, and trustworthy AI


• Health data science (e.g., neuroscience, biomedicine, and genetics/genomics)


• Data and Society/Democracy (e.g., ethics, social justice, and the impact of data on society)


• Sports Data Science and human development (e.g., human movement, computer vision, and analytics)


• Data science in education (e.g., AI in the classroom, data-driven educational strategies, data-centric teaching methods)

When applying, candidates should detail their research expertise and interests, their instructional experience, preferred teaching domain, and other scholarly interests.