Professor of Data Science

William & Mary · Mary

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Required Qualifications:

• Applicants must hold a Ph.D. in Data Science, Computer Science, or a closely related field at the time appointment begins (August 10, 2027).
• Previous experience in teaching and mentoring successful undergraduate and graduate research. 
• The successful applicant will demonstrate a track record of externally-funded research, extensive scholarly achievement, and intellectual leadership and international visibility in the broader artificial intelligence and data science research community.

Preferred Qualifications:

• Evidence of scholarly leadership in the broader field, such as service for major conferences or journals.
    

Applicants must apply online at https://go.wm.edu/VphZB3. All applications must consist of the following materials for full consideration; (Please note: No more than 5 individual documents may be uploaded into the upload field provided. Each individual file must not exceed a size limit of 5MB. If necessary, candidates can compress multiple files into a single zip file for upload):

• Curriculum vitae;
• Cover letter;
• A statement of teaching interests;
• A statement of research interests

Applicants are encouraged to reflect on teaching and mentoring practices that advance academic excellence and success of all learners/scholars in Data Science in any of the aforementioned required documents. 

Candidates who reach the long-list (Zoom) interview stage will be prompted to submit online the names and email addresses of three references who will be contacted by the system with instructions for how to submit a letter of reference.

Duties:

• Research, teaching, and service to the university.
• The applicant is expected to establish and maintain a vibrant externally funded and internationally recognized research program that inspires a highly motivated undergraduate student body and supports Master’s and Ph.D. students. 
• Teaching expectation is one course per semester. Successful applicants must possess the skills to teach compelling courses in natural language processing and other lecture and seminar-style courses, and to contribute to the development of an emergent curriculum around generative artificial intelligence.

For full consideration, submit application materials by the initial review date, 11/13/2026. Applications received after the initial review date will be considered if needed. All required supplemental application materials mentioned above must be clearly identified and included with your application for full consideration. Review of applications is ongoing. Applications received after the initial review date will be considered if needed.

Information on the degree programs in the Department of Data Science may be found at ds.wm.edu. 

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EEO is the Law. Applicants can learn more about William & Mary’s status as an equal opportunity employer by viewing the "Know Your Rights" poster published by the U.S. Equal Employment Opportunity Commission. https://www.eeoc.gov/know-your-rights-workplace-discrimination-illegal

Background Check: William & Mary is committed to providing a safe campus community. W&M conducts background investigations for applicants being considered for employment. Background investigations include reference checks, a criminal history record check, and when appropriate, a financial (credit) report or driving history check.

Applicant Notice: By applying for this position, you consent to William & Mary's use, review, and retention of your application materials for recruitment and employment purposes. Materials may be shared with authorized individuals participating in the hiring process and will be managed in accordance with applicable laws and university policies.

Probationary Period Notice: Individuals selected for full-time staff positions will be required to complete a twelve (12) month probationary period in accordance with university policy.

Remote Work Disclaimer: Remote work eligibility is not guaranteed and is subject to approval. Employee eligibility depends on the likelihood of the employee succeeding in a remote work arrangement and the supervisor’s ability to manage remote workers. Departments and/or Human Resources may amend, alter, change, delete, or modify eligibility. 

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