Pachama is on a mission to solve climate change by restoring forests, nature’s carbon sinks. Pachama harnesses satellite imaging with artificial intelligence to measure carbon captured in forests. This technology enables remote verification and monitoring of forests to provide a new standard of assurance in carbon markets. Through the Pachama platform, we connect responsible companies and individuals with carbon credits from projects that are protecting and restoring forests worldwide.

Pachama is building scalable tools to enable more forest projects around the world to participate in carbon markets, promoting more restoration and protection of forests and a healthier planetary future.

The Role

Pachama is looking for an energetic team member to support our team in analyzing and reporting on forest carbon offset projects using new technology. This role would help to evaluate carbon projects while being a spokesperson for science and sound carbon principles to our stakeholders.

This role is remote (as is the entire Pachama team). We believe in working where you want your life to be. However, being within 3 hours of Pacific time is slightly preferred.

Contract work and part-time availability may be possible.


  • Develop and apply remote sensing models to validate forest carbon offset projects.
  • Analyze a combination of remote sensing outputs and project documentation to draw conclusions about forest carbon projects.
  • Develop written reports of findings for Pachama and its stakeholders.
  • Assist with collection and processing of forest inventory datasets.

Some things that might be helpful in this role

  • Familiarity with forest carbon projects or carbon offset protocols.
  • Forest mensuration skills. Familiarity with forest sampling techniques, allometry, baseline calculations, etc.
  • Familiarity with different remote sensing datasets and model development.
  • Ability and willingness to communicate about complex topics to non expert audiences (both in writing and in person)
  • Strong python skills. Experience with common python geospatial libraries (e.g. gdal, rasterio, shapely, fiona).
  • Experience with GIS tools (e.g. QGIS).