Data Scientist - Forest Futures Lab - Western Fire & Forest Resilience Collaborative - Cary Institute of Ecosystem Studies

We seek a creative, innovative data scientist to join the Western Fire and Forest Resilience Collaborative. The Collaborative is engaging 10 science teams and a diverse array of decision makers to co-create and implement a research program that will ensure the predictive science of fire ecology and forest resilience can support effective strategies to the fire crisis. The Collaborative is led by the Forest Futures Lab at Cary Institute of Ecosystem studies, where the data scientist would be based. We expect the Collaborative to run for the next 5 to 10 years.

The data scientist will be responsible for using process-based simulation and AI-based analysis of big data (e.g., remote sensing) to help address decision-maker driven questions. The data scientist will also work with the Collaborative to develop interactive, engaging, and accessible data summaries and visualizations of scientific data for non-academic audiences. This may include web applications and spatial products developed with Python, R or other programming languages.

This position will be supervised by the Collaborative Director, Dr. Winslow Hansen, and Deputy Director, Dr. Crystal Raymond. Cary Institute of Ecosystem Studies is the world’s premier think tank on ecosystem science. It is a uniquely collaborative, supportive, and welcoming institution. The successful candidate will be provided resources for travel related to the project and will have many opportunities for professional development. For additional information on the Forest Futures Lab, visit http://forestfutureslab.org/. For additional information about Cary Institute of Ecosystem Studies, visit https://www.caryinstitute.org/.

Duration: This is a full time, exempt, fully benefited position for one year and is renewable for additional years contingent upon successful performance. Annual salary ranges from $60,000 - $85,000 depending on experience plus a highly competitive benefits and time-off package. A start date before December 1, 2024 is preferred.

Location: Millbrook, NY is preferred but remote work will be considered for the right candidate

Qualifications: Did you know most successful candidates do not meet all qualifications? If you have some of these skills, please consider applying. Applicants must have completed a master’s degree in computer science, data science, Earth-system science, ecology, or climate science, and have 3 years of professional work experience. The ideal candidate will have experience with both ecology and data sciences We seek candidates with strong analytical skills and experience in geospatial analysis, web applications, and programming. Strong expertise in Python, R, Javascript, and/or C++ is required. Experience working in an HPC and/or cloud environment is preferred. Strong oral and written communication skills are a plus. We are particularly seeking candidates who have experience working at the boundary of science and decision making and are passionate about using data for real-world impact.

To Apply: Please complete an online application at https://www.caryinstitute.org/about/careers-cary. As a single PDF, please also upload a cover letter that describes your current and future research interests (no more than 2 pages in length), your CV, and the names and contact information of three references using the upload resume link on the application website. Applications will be reviewed starting September 9^th, but the position will remain open until filled. All candidates must be authorized to work in the U.S. Finalist candidates must successfully complete a post-offer, pre-employment background check.

The Cary Institute is an Equal Employment Opportunity (EEO) and Affirmative Action (AA) employer. It is our policy to provide equal employment opportunities to all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, familial status, protected veteran or disabled status, or genetic information.