Assistant, Associate, or Full Professor of Analytics at Furman University in Main Campus

Job description

Welcome to Furman University's Career Site! IMPORTANT :​ Load all documents in the My Experience area under Resume/CV . This may include your resume/C.V., cover letter, unofficial transcript, teaching philosophy, etc. Please wait for all documents to finish uploading before clicking Next. If the process times out you will lose your progress, so please ensure you have all necessary documents available before starting. The drop-down lists only display so many options: please type into a drop-down list to search for your option. For example: in Field of Study, you may need to enter your field name. If you have no field, type "None." If you have previously applied, make sure your information is current as you can transfer it to another application. Prior to submitting your application, verify all information for accuracy and ensure you have uploaded all appropriate documents. Once submitted, you will not be able to edit your application other than to change your contact information. If you have any questions or need additional assistance, please contact the Office of Human Resources via email at recruitment@furman.edu or by calling 864-294-3103. Job Title: Assistant, Associate, or Full Professor of Analytics Job Family: Furman Faculty Full-Time/Part-Time: Full time Pay Type: Salary Department: Business/Accounting Job Summary: The Business and Accounting Department at Furman University invites applications for a tenure-track position in Analytics at the rank of Assistant, Associate, or Full Professor. This position is one of three newly approved faculty hires in business and accounting and will commence on August 1, 2027. Candidates with a strong interest in data analytics education within a nationally ranked liberal arts and science setting are encouraged to apply. Successful candidates are expected to excel in teaching at the undergraduate level, mentor and advise students, and conduct original research leading to peer-reviewed publications or other significant scholarly contributions to the discipline. The ideal candidate will also demonstrate the ability to work with students from diverse backgrounds and contribute to Furman’s commitment to expanding access and opportunity within our rich learning culture. The teaching load is five courses per year. In addition to the introductory and intermediate business analytics courses, the successful candidate will have the opportunity to teach advanced data analytics electives related to their area of expertise. Job Description: A Ph.D. or D.B.A. in data analytics, operations, business, or a related discipline is required by August 1, 2027; ABD candidates will be considered, provided the degree is conferred before that date. Prior university-level teaching experience and corporate or industry experience are highly preferred but not required. The teaching load is five sections per academic year. In addition to the introductory business analytics course, the candidate will have the opportunity to teach advanced business analytics electives related to their area of expertise. Successful candidates are expected to excel in teaching at the undergraduate level, mentor and advise students, and conduct original research leading to peer-reviewed publications or other significant scholarly contributions to the discipline. Duties also include service to the department and university. The Business and Accounting Department offers bachelor’s degrees in Accounting, Business, and Finance . Within the Business major, students can select a track in Entrepreneurship, Marketing, International Business, or Business Administration. The Business and Accounting Department also participates in and supports the Data and Decision Analytics major, and the interdisciplinary minors in Data Analytics and Visual Strategy. The ideal candidate will also demonstrate the ability to work with students from diverse backgrounds and contribute to Furman’s commitment to expanding access and opportunity within our rich learning