Assistant Professor of Data Science and Statistics

University of Richmond

4d • Onsite

About The Position

The Data Science and Statistics Program in the School of Arts & Sciences at the University of Richmond invites applications for a full-time tenure-track position at the rank of Assistant Professor, to begin in the 2027-2028 academic year. The Data Science and Statistics Program is an interdisciplinary program rooted in the University's liberal arts mission with affiliated faculty from departments across the School of Arts & Sciences. The program offers a minor in data science and statistics and teaches a core sequence of courses in statistical modeling, data science, probability, and statistical learning. The appointment will be housed in the Department of Mathematics and Statistics, with all teaching responsibilities within the interdisciplinary Data Science and Statistics program. The successful applicant is expected to commit to highly effective teaching at all levels of the undergraduate curriculum and to maintain a vigorous research program. Candidates from all subfields of data science and statistics will be considered. Rather than seeking a particular area of specialization, we are looking for candidates who can teach statistical and computational methods to students from a wide range of majors and who are eager to collaborate with the program's affiliated faculty across the natural sciences, social sciences, and humanities.

Requirements

• Ph.D. in statistics, data science, computer science, information science, mathematics, or a related field of research by the time of appointment.

Responsibilities

• Commit to highly effective teaching at all levels of the undergraduate curriculum.
• Maintain a vigorous research program.
• Teach statistical and computational methods to students from a wide range of majors.
• Collaborate with the program's affiliated faculty across the natural sciences, social sciences, and humanities.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree