This appointment is a 12-month, tenured or tenure-track position at the Assistant or Associate Professor level with apportionments in teaching and research in the Department of Statistics in the Institute of Agriculture and Natural Resources (IANR) at the University of Nebraska-Lincoln.

The successful candidate will be the Director for the Statistical Cross-disciplinary Collaboration and Consulting Lab or SC3L, also known as the (Statistical) Help Desk. The SC3L is staffed by PhD students who provide statistical support to researchers within IANR and other UNL researchers, including faculty at research and extension centers and external clients. The successful candidate will oversee the PhD students, as well as develop independent collaborations that lead to publications in high-quality, high-impact, peer-reviewed journals. The successful candidate will oversee the continued growth of the SC3L to meet the increasing need for statistical consulting and collaboration. This includes creating scholarly and innovative learning programs and tools to educate researchers outside of statistics. Through the SC3L, the successful candidate will also train PhD students to function as independent consultants and educate researchers outside of statistics on how to effectively use statistical support. The successful candidate will participate in scientific meetings and other appropriate professional activities.

The successful candidate will be expected to teach the equivalent of three standard courses per academic year at the undergraduate, Master’s and PhD level as assigned by the Department Chair. In particular, MS and PhD courses in the practice and scholarship of Statistical Consulting and Collaboration. In addition, the successful candidate will participate in program and curriculum development. Specific course assignments may be changed over time according to academic unit’s need.

The appointee will also contribute, as an effective scholar and citizen of a land-grant institution, to the integrated mission of home units (e.g., department, center), including supporting student recruitment, IANR science literacy initiative, and beyond. Additional responsibilities of the academic appointment are to participate in retention and placement activities and teaching outcomes assessment, instructional improvement, and teaching scholarship.

Recognizing that diversity within a context of inclusivity enhances creativity, innovation, impact, and a sense of belonging, the Institute of Agriculture and Natural Resources (IANR) and Statistics are committed to creating learning, research, Extension programming, and work environments that are inclusive of all forms of human diversity. We actively encourage applications from and nominations of qualified individuals from underrepresented groups.

Minimum Qualifications
• Ph.D. in statistics, data science, computer science, engineering, or closely related field.
• Experience with consulting or applications.
• Computational proficiency.
• Excellent communication skills.
• Teaching experience at the university level.

Preferred Qualifications
• Demonstrated experience in statistics or data science in a field of importance to IANR.
• Evidence of ability and interest in modern analysis techniques and data types.
• Experience managing a shared resource, core, or similar group.
• Interest in working with diverse or underrepresented communities or groups.
Review of applications will begin January 17, 2022 and continue until the position is filled or the search is closed. To view details of the position and create an application, go to, requisition F_210223. Click “Apply to this job” and complete the information form. Attach 1) a letter of interest that describes your qualifications for the job, anticipated contributions, and the value you place on diversity and your anticipated contributions to creating inclusive environments in which every person and every interaction matters (2 page maximum; see for guidance in writing this statement); 2) your curriculum vitae; and 3) contact information for three professional references.

As an EO/AA employer, qualified applicants are considered for employment without regard to race, color, ethnicity, national origin, sex, pregnancy, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, marital status, and/or political affiliation. See