Professor of Data Science, Academic General Faculty Teaching Track

School of Data Science at the University of Virginia is assembling a world‑class faculty with broad expertise in data science to foster and advance our Undergraduate, Master, and PhD programs. We are searching to fill multiple open rank professorships in data science on the Academic General Faculty Teaching Track
. At the School of Data Science, Academic General Faculty on the Teaching Track instruct classes (in‑person and online), conduct educational scholarship, engage in grant‑driven research, and advise and mentor students. Exceptional candidates will demonstrate experience with, or the potential for, sustained scholarship in data science education or a research specialty in an academic or industry setting. Academic General Faculty are afforded significant stability with long‑term contracts, support for professional development, and leadership opportunities (e.g., all program directors are currently academic general faculty on the teaching track).

We seek candidates with an innovative spirit who have strong skills in data science pedagogy, instruction, and advising. Faculty will have the opportunity to help shape the culture of a new school, develop modern pedagogy, and grow the discipline of data science in national and international forums.

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 instructional experience, preferred teaching domain, and scholarly interests and/or research activity.