Head of Science and Information

Company: Upstream Carbon

Role Overview: Are you a visionary scientist ready to transform how we protect and restore our planet? Upstream Carbon is seeking a pioneering Head of Science to revolutionize land conservation through cutting-edge machine learning and geospatial technology. This is not just a job—it's a chance to create the future of ecosystem protection. Join us and become a founding leader, shaping the technical and scientific vision of Upstream Carbon. This pivotal role involves driving innovation and ensuring our platform and client projects are built on a solid scientific foundation. You will tailor and advance our proprietary machine learning algorithms, develop new ecosystem and land use models, and directly interact with clients to align our scientific innovations with market needs. Your strategic input will significantly impact our team, product, and business model.

About Upstream Carbon: Upstream Carbon uses machine learning and geospatial analysis to drive land conservation, clean energy site selection, and sustainable development. The platform’s advanced algorithms assess properties on their suitability for climate-smart projects, aiding in carbon project site evaluations and public sector land use planning. Founded in 2023 and self-funded through 2024 on geospatial software licensing and services revenue, Upstream Carbon was recently awarded federal funding to scale out its geospatial intelligence and identify landowners of suitable sites for forest conservation and carbon projects across the eastern USA.

Benefits:

• Founding team equity stake with substantial potential

• Highly competitive compensation package including competitive salary ($145,000), health benefits, flexible vacation, and a voice in deciding future employee benefits

• Pioneering a mission that directly combats climate change and protects critical ecosystems in North America

• Remote-first work environment with meaningful in-person collaboration

Responsibilities:

• Scale and enhance existing machine learning algorithms and geospatial data pipelines, efficiently leveraging raster and vector data for new regions in the US

• Upgrade existing data sources and develop new methods by working with partners (scientific community, public sector agencies, corporations) to more accurately estimate carbon pools (forest carbon, soil carbon, wetlands, etc.) on potential sites and threats to those carbon pools

• Evaluate and implement methods to quantify conservation co-benefits “beyond carbon” including biodiversity, water resources, food production, and ecosystem connectivity

• Apply machine learning, statistical, and other techniques to create proprietary high-resolution input datasets from established open source and public data

• Develop novel data visualizations to communicate ecosystem threats, opportunities, and carbon revenues to landowners, corporations, and third-party verifiers

• Draft and review methodological publications intended for public audiences

• Recruit and develop new hires for research and engineering roles

Ideal Candidate:

• Is a nature enthusiast

• A creative problem-solver with a hacker mindset, eager to deploy new technologies in AI and geospatial data

• Demonstrated success implementing supervised machine learning models and skilled at qualifying machine learning solutions for real-world problems

• Fluent with Python (Geopandas, GDAL, Rasterio, etc.) and R for geospatial data preparation, analysis, and machine learning application

• Familiar with hosted mapping tools (Leaflet, Mapbox) and the broader geospatial technology ecosystem

• Experience with grant writing, publications, and/or patent filing

• Experience leading a team

• Previous work in conservation, ecology, geoscience, public sector, or related fields is desirable

• Graduate degree in a scientific field

How to Apply: Submit your resume along with a brief description (280 characters max) of an ecosystem you are passionate about and believe deserves restoration or permanent conservation.

Location: Boston, MA (preferred, but flexible for the right candidate)

Sector

Conservation and Real Estate