This Position is for an Assistant Professor for School Support and Data Services with a work location in University, MS.

Position Description

The Assistant Professor for Scholar Support and Data Services serves as a member of the Scholar Support and Data Services team, which works with other library departments, campus partners, faculty, students, and staff to promote, create, and support the use of digital scholarship tools and methodologies in interdisciplinary scholarly research. This position provides expertise to support quantitative and mixed research methodologies, especially in the context of computational research, coding for researchers, and guidance on data management.

Responsibilities

Provide in-person and virtual support for research and teaching that utilizes computational tools and methodologies.

Provide in-person and virtual support to faculty, students, and staff on research data management, best practices, and University policies.

Develop and implement library instruction on computational approaches to research through reproducible code (e.g. Python, R).

Assist the university community in the use of proprietary (e.g., Qualtrics, SPSS, Dedoose) and open-access software (e.g., JASP, Python) used for qualitative and quantitative data collection and analysis.

Collaborate with others to provide customized discipline- and context-specific training on advanced data-related topics.

Evaluate and expand upon existing data services by developing partnerships and becoming integrated into the institutional research environment.

Advocate for and represent the research needs of faculty and students to the library.

Coordinate ongoing assessment of instruction in the department.

Maintain knowledge and expertise of current and emerging trends and policies in data management, computational research methods, and related topics.

Provide effective communication and outreach related to the efforts of the Scholar Support and Data Services team.

Participate in efforts to expand open initiatives and open access, open science, open data, and the use of open software.

Serve on the Libraries’ committees and functional teams related to areas of responsibility.

Contribute to the profession at a level appropriate for a tenure-track faculty member of the university.

Position Details

Appointment: 12 Month

Assignment Type: Tenure Track

Minimum Qualifications

MLS or other equivalent advanced degree

Demonstrated experience and expertise in research, including quantitative and mixed methodologies and the analysis of quantitative/mixed data using proprietary or open-access research software.

Experience teaching or providing instruction in individual, small group, or classroom settings.

Ability to work both independently and collaboratively as part of a team within a culturally diverse user community of faculty, students, administrators, and the general public.

Excellent oral and written communication skills, especially the ability to communicate complex technical and data science concepts to both general and specialized audiences.

Demonstrate a commitment to fostering and promoting the values of diversity and inclusion.

Interview Requirements: Any candidate who is called for an interview must notify the Department of Equal Opportunity/Regulatory Compliance in writing of any reasonable accommodation needed prior to the date of the interview.

Background Check Statement

The University of Mississippi is committed to providing a safe campus community. UM conducts background investigations for applicants being considered for employment. Background investigations include a criminal history record check, and when appropriate, a financial (credit) report or driving history check.

EEO Statement

The University of Mississippi provides equal opportunity in any employment practice, education program, or education activity to all qualified persons. The University complies with all applicable laws regarding equal opportunity and affirmative action and does not unlawfully discriminate against any employee or applicant for employment based upon race, color, gender, sex, pregnancy, sexual orientation, gender identity or expression, religion, citizenship, national origin, age, disability, veteran status, or genetic information.