Map of Life is seeking an intern to participate in a global biodiversity research and conservation initiative at Yale University. This is a great opportunity for recent graduates looking to gain experience in spatial ecology and conservation. This position is a 12-month long internship with the possibility for extension.

Built on a scalable web platform geared for large biodiversity and environmental data, Map of Life endeavors to provide ‘best-possible’ species range information and species lists for any geographic area. Map of life aims to support effective and global biodiversity education, monitoring, research and decision-making by assembling and integrating a wide range of knowledge about species distributions and their dynamics over time.

Position responsibilities
In this role, you will be working closely with a team of ecologists and software engineers to create biodiversity products that will be used to inform research, conservation decision-making, and education. You will learn how to use GIS tools for digitizing and analyzing biodiversity data. Map of Life’s database hosts over 1 billion records, nearly 100,000 species and over 555 million records, including almost all terrestrial vertebrates, dragonflies, bumblebees, butterflies, and plant species. Contributing to Map of Life’s comprehensive database requires keen attention to detail and a mindset centered around scientific integrity. Interns will learn the nuances to integrating biodiversity data, and how that is key to answering questions at a global scale.
Position requirements
Demonstrated familiarity with ecological concepts through coursework and/or work experience; Strong interest in biodiversity and conservation; Desire to increase knowledge in spatial ecology and conservation; Demonstrated experience working as part of a team; Effective communication skills; Demonstrated experience in GIS; Excellent time management skills; Ability to multi-task, prioritize, and work under pressure; Detail-oriented.
Preferred Skills
Familiarity in one programming language (R, python preferred); Comfortable with data management principles and basic statistics; Ability to plan, organize, and manage a large volume of varied work in a complex, fast-paced environment.

To apply, please send your CV and cover letter to [email protected]