Environmental Data Science Specialist

The Virginia Tech Center for Ecosystem Forecasting (www.ecoforecastprojectvt.org) is recruiting a data scientist to build predictive models across multiple ecosystem types. We seek an energetic and enthusiastic team member to join our innovative and dynamic research center to help develop real-time ecological forecasting models, software, and computing infrastructure to inform day-to-day environmental resource management. This position will be a core member of our research center, which includes multiple faculty members, staff, data scientists, post-docs, and students working at the intersection of environmental science and data science.

This data scientist position will play a critical role in supporting the Center’s mission to: analyze environmental data; build and share ecological models and software; create and assess a diversity of forecasting methods; translate forecasts for decision support; and engage with forecast users. This position will provide many opportunities for skill development and professional growth within our interdisciplinary collaborative team, which integrates team science, community engagement, and education.

Responsibilities for this position include: the development and deployment of predictive models; management of cloud-based environmental data and workflows; development of open-source software and open training materials; and supporting the computational needs of the team.

The position can start at Virginia Tech’s Research Specialist 1 or 2 level depending on prior experience, with a salary commensurate with experience and expertise. This position is in-person, based on Virginia Tech’s campus in Blacksburg, Virginia, USA.

Candidates should submit a cover letter addressing how they meet the required and preferred qualifications described below that includes a link to their software portfolio (e.g., GitHub profile), a resume/CV detailing relevant work experience, and a list of three references with their contact information. This position is funded for at least two years, contingent upon satisfactory performance reviews. The position will remain open until filled. Application review will begin on 02/15/2024.