Jobs at eBird

Team eBird is based out of the Cornell Lab of Ornithology in Ithaca, New York. We are a passionate team that includes bird-heads, application developers, user interface and design experts, and database gurus who are committed to building tools to deliver high quality data that can be used for science and conservation. We work collaboratively with many other teams at the Cornell Lab of Ornithology including the Macaulay Library, Bird Populations Studies, Conservation, Education, and Communications and positions from those groups that are most directly related to eBird are also included here.

Please see below for the current jobs that are available with eBird (and sometimes with other related projects). View all Cornell Lab job opportunities here.

Current Job Postings

Data Science Developer

Cornell Lab of Ornithology, College of Agriculture and Life Sciences
Ithaca, NY

See full listing and apply here

The Data Scientist Developer will collaborate with data engineers, ecologists, scientists, and data visualization developers at the Center for Avian Population Studies to: support data science activities on production and research systems to advance large-scale production workflows; transform and integrate data products for novel applications and visualizations; derive and summarize insights and inferences from large and complex datasets; and develop analysis for scientific research projects. This will include the use of both machine learning and statistical methods to design, implement, test, validate, interpret, and quantify uncertainty of models for prediction and inference based on citizen science.

Research Associate: Applied Quantitative Ecologist

Cornell Lab of Ornithology, College of Agriculture and Life Sciences
Ithaca, NY

See full listing and apply here

The Center for Avian Population Studies, Conservation Science Program is seeking an applied quantitative ecologist to augment ongoing applied research and conservation science on bird populations, and to develop and apply quantitative methods in population ecology. The Applied Quantitative Ecologist will play a leading role in the Conservation Science Program in the development and application of novel statistical methods to address pressing applied research questions and critical information needs of partners and collaborators, with a specific focus on conservation practitioners and decision-makers across a broad range of sectors. More specifically, this position will focus on innovative approaches to model populations and communities (e.g. joint-species distribution models) using presence-absence biological monitoring data (e.g. eBird data). In addition, this position will focus on developing methods geared towards using birds as indicators of ecosystem health, needed to benchmark and track the progress of biodiversity conservation initiatives (e.g. regenerative agriculture).  This position will also assist in leading the development of new data products using lab resources (e.g., eBird data), aimed to help evaluate and guide conservation action around the globe.

Research Associate: Data Scientist

Cornell Lab of Ornithology, College of Agriculture and Life Sciences
Ithaca, NY

See full listing and apply here

The eBird Status and Trends (S&T) project is seeking a Data Scientist to join our team. This work is at the interface of machine learning, statistics, and ecology and will span a range of topics from sampling design, exploratory analysis, visualization, and discovery to prediction, validation, quantification of uncertainty, and statistical inference. The Data Scientist will play a leading role researching and developing the quantitative analyses used to generate and apply S&T data products. You will work closely with other team scientists to improve and extend models of species’ distributions, abundance, and trends, as well as developing new methods to create additional data products.  The Data Scientist will also participate in projects demonstrating how S&T data products can be used in impactful ecological and conservation applications and decision-making activities.