Research Scientist in Environmental Sciences Sciences

The Department of Environmental Sciences at the University of Virginia (UVA) invites applications for a Research Scientist (Information Manager) to lead information and data management for the Virginia Coast Reserve Long Term Ecological Research project (VCR LTER).

This is a full‑time position based at UVA in Charlottesville, VA, with periodic travel required to the VCR LTER site at UVA’s Coastal Research Center on Virginia’s Eastern Shore.

The VCR LTER Information Manager (Research Scientist) oversees the full lifecycle of diverse ecological data, from acquisition and quality control through documentation, standardization, publication, and preservation. The main priority is stewardship of core LTER datasets such as long‑term biological, oceanographic, hydrologic, geologic, and meteorological data. The IM also collaborates and advises researchers to design effective data workflows, harmonize data to support synthesis and modeling efforts, and translate information management best practices into practical, researcher‑friendly solutions.

• Data stewardship and publication: manage, curate, and archive VCR LTER datasets and associated metadata; ensure compliance with federal and LTER data management requirements, including timely public data release; publish datasets to the Environmental Data Initiative (EDI), maintain accessible data catalogs on the VCR LTER website, and coordinate deposition in national repositories; develop and maintain QA/QC workflows and ensure compliance with data documentation standards.
• Systems, workflows, and infrastructure: maintain and enhance data‑management systems supporting long‑term field observations, sensor data, and derived data and modeling products; work with existing infrastructure to maintain continuity while contributing to modernization; develop reproducible data processing pipelines using scripting and workflow tools; provide technical oversight for long‑term deployed sensor instruments and data acquisition systems; coordinate with UVA Information Technology Services and project partners on system reliability and security;
  
  maintain the VCR LTER website, including programming web services for researchers.
• Research collaboration and training: work directly with faculty, postdocs, students, and staff to support data organization, documentation, and analysis; provide training for data‑management best practices and reproducible workflows; assist with data access, integration, synthesis, and modeling; help researchers evaluate AI tools to support research and information management; contribute to project reports, proposals and publications; maintain internal and external personnel and project databases.
• Network engagement and professional development: represent VCR LTER within the LTER Network and communicate effectively with Information Managers at other LTER sites; participate in national meetings, working groups, and professional development activities; engage with evolving community standards for ecological data and open science.

Requires a doctoral degree or a master’s degree with significant experience, in ecology, environmental science, data science, computer science, information science, or a related field, by the start date.

• Experience working across the research data lifecycle, including data cleaning, quality control, documentation, and publication.
• Proficiency in at least one scientific programming language (e.g., R or Python).
• Experience managing or working with structured datasets and relational databases (e.g., SQL).
• Strong organizational skills and attention to detail.
• Excellent written and verbal communication skills, with the ability to work effectively with scientists across disciplines, institutions, and career stages.

• Ability to navigate and coordinate complex systems and find efficient solutions to challenging problems.
• Background or training in ecology, environmental science, or related natural sciences, or demonstrated experience working closely with ecological or environmental data.
• Familiarity with long‑term observational or…