Geospatial Consultant

Posted to IASSIST on: 2015-10-16

Employer: The University of Virginia

Employer URL:


The University of Virginia Library establishes deep connections with faculty and students in ways that foster excellence in current and emerging research, scholarship, teaching and learning. The Geospatial Consultant will support that mission by collaborating with a cohort of librarians plus data science and geospatial data experts to: shape our public service model for geospatial and geostatistical consultation and delivery; support research and teaching that uses geospatial and geostatistical information; identify emerging needs in these fields and envision new services in response; and develop and strengthen relationships among the library, faculty and students, and other centers on campus, positioning the library as a key partner. Responsibilities of the successful candidate will include developing and providing training to faculty, students, and staff on obtaining, creating, and using geospatial data for research; consulting with faculty and students on projects incorporating geospatial data, tools, and analysis; collaborating on Library and University initiatives to develop spatial delivery environments and geospatial information management systems; and engaging with the scholars on grounds to increase awareness of the Library’s geospatial, data, and subject expertise.


  • A Masters’ degree in a geospatial or geostatistical discipline or relevant discipline. 
  • Experience in public service. Experience in a position focused on supporting academic uses of geographic information systems. 
  • Teaching experience in GIS and/or geostatistical methods and practices.  Competence in presenting complex technical knowledge to undergraduate and graduate students as well as faculty and staff.  
  • Excellent communication skills.    
  • Familiarity with either (1) ESRI software, including ArcGIS Desktop and ArcGIS Online and in open-source solutions for GIS data management and use, or (2) concepts and theories of spatial statistics and implementation of spatial models in R or SAS.   



  • Experience in a University setting. 

Archived on: 2015-10-16