Career Opportunity / 

Data Scientist - Remote Sensing of Individual Trees

Yale University & Map of Life Rapid Assessments - XPRIZE

Data Scientist - Remote Sensing of Individual Trees 

The Yale Center for Biodiversity and Global Change is seeking an enthusiastic data scientist with a background in photogrammetry and geospatial data processing to develop workflows for single tree identifications for biodiversity science and conservation applications. 


The Yale Center for Biodiversity and Global Change and its flagship Map of Life (MOL) project are advancing new technologies for the rapid documentation of local biodiversity. Map of Life delivers research and information about the distribution and conservation of biodiversity in a changing world to science, policy, business, local communities, and educators. Map of Life Rapid Assessments takes the power of Map of Life to the local scale and combines with novel survey technologies to deliver relevant local biodiversity insights.  

Map of Life Rapid Assessments inventories local species communities with remotely operated visual and acoustic sensors in combination with expert knowledge and AI. Our teams deploy a fleet of drones in areas of high biodiversity richness and work to collect visual and acoustic samples within the environment. Our subsequent analysis then combines AI with human-in-the loop approaches to identify species. Our approach and team have now been selected as one of the six finalist teams in the XPRIZE Rainforest competition

A key product of our solution are dense, very high-resolution maps depicting individual trees. Such maps serve as a basis for subsequent analytics and inference on ecological processes in the vicinity of the area. You will contribute to the work through software implementations, applying techniques from photogrammetry, GIS, and machine learning.

Position duration is 6-12 months for 20-40 hours per week. Long-term employment opportunities may be available following the initial appointment. A combination of remote and in-person work can be accommodated. Position available to start immediately.

Your Profile

Candidates should bring:

  • A BSc. degree or higher in Computer/Data Science, Machine Learning, Ecology, or a similar scientific discipline.
  • Excellent knowledge and experience in photogrammetry, software development (Python), GIS software (QGIS, ArcGIS) and APIs (GDAL, etc.).

Application Instructions

We are excited to receive your application! Please send the following documents to [email protected]:

  • Cover letter
  • CV or resume
  • Contact information for two referees

For more information and questions about the position or advertisement please contact Benjamin Kellenberger ([email protected]). To view our other open positions please visit

We strongly encourage members of underrepresented groups in the sciences to apply. Historical and ongoing social inequities rooted in racism, sexism, ableism, and other forms of discrimination result in the continued and widespread exclusion of marginalized groups from academic spaces. At our Center, we strive to support individuals from diverse backgrounds and to create a safe and inclusive community to counter these legacies of discrimination within the ecological and environmental sciences. We are actively committed to building a team and community where individuals representing a variety of paths to the sciences are brought together to foster a community of learning and collaboration. We hope that our commitments and actions create a more supportive and inspiring environment for individuals and contribute to a more inclusive and equitable future for our field.

Yale University offers a thriving and growing international community of young scholars in ecology, evolution and global change science in the Department of Ecology and Evolutionary Biology, the Yale Institute for Biospheric Studies, the Peabody Museum, and the School of the Environment. Yale University is located two hours north of New York City with many public transportation options to explore surrounding cities.