Applied Soil Scientist

about 1 year ago
Full time role
Remote · Boulder, CO, US... more
Remote · Boulder, CO, US... more

Company

Welcome to Perennial. Perennial is building the world’s leading verification platform for soil-based carbon removal. Our vision is to unlock soil ...

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Job Description

Welcome to Perennial.

Perennial is building the world’s leading verification platform for soil-based carbon removal. Our vision is to unlock soil as the world’s largest carbon sink. To do that, we are building trusted standards, tools, and technologies to help verify climate-smart agriculture.

Perennial uses the world’s most advanced remote measurement technology for soil carbon sequestration and emissions. We fuse machine learning, ground observations, and satellite data to map soil carbon and land-based GHG emissions at continent-level scales. This technology is powering the future of climate-smart agriculture and helping the food supply chain decarbonize.

At Perennial, you will work in a mission-driven and collaborative environment alongside a diverse team with backgrounds spanning science, technology, carbon markets, and agriculture.

Our offices are located in Boulder, CO USA. We are a fully-flexible company for remote and hybrid work.

We’re venture-backed by mission-aligned investors including Temasek, Bloomberg, Microsoft Climate Innovation Fund, SineWave Ventures, Alumni Ventures Group, and Collaborative Fund. Our work has been featured by TIME, Forbes, WIRED, NASA, and Fast Company.

About the Role:

As an Applied Soil Scientist, you will contribute to the development of Perennial’s data-driven methodology to quantify organic carbon stocks in agricultural soils, including row-crop agriculture and rangeland. We encourage applications from individuals with a wide range of experiences, including but not limited to: (1) design and implementation of field sampling of organic carbon content and other soil properties in rangelands; (2) biogeochemical simulation of organic carbon stock fluxes; (3) spatial or data-driven analysis of carbon stocks and stock changes in rangeland soils; (4) remote sensing or other data-science analysis focused on spatial modeling and prediction for regenerative agricultural ecosystems. For this particular position, experience with rangeland ecosystems is required.

If you have pre-existing academic commitments, we may be able to accommodate a part-time contract structure. 

What You'll Own:

  • Contribute to the design of field campaigns to measure soil properties, including soil organic carbon and bulk density, especially in rangeland soils.
  • Bring knowledge of current best practices, data sources and relationships that strengthen Perennial’s ability to quantify carbon content in rangeland soils.
  • Develop and test new techniques within soil carbon modeling, LULC change detection, soil sample stratification, and/or digital soil mapping.
  • Codify your analyses into features for Perennial’s core modeling/product offering; work with software engineers to productionize and scale your methods.
  • Advise data science and software engineering teams in ways that strengthen data-driven models of carbon content.
  • Communicate your findings and developments to a wide range of audiences, including customers and the wider scientific community, both visually and verbally.

What You'll Bring:

  • PhD in soil biogeochemistry, soil and crop science, agroecology, or related field.
  • Experience working with or advising regenerative agricultural programs.
  • Deep understanding of the dynamics of soil organic carbon stocks, soil properties, or land management of rangeland ecosystems.
  • Strong scripting experience (R acceptable, Python preferred).
  • Experience working with agricultural data from a variety of sources.
  • Familiarity with remote sensing data.
  • A strong belief in the potential of regenerative agriculture to improve farmer livelihoods, ecosystem health, and combat climate change. 

What will make you stand out:

  • Strong experience analyzing remote sensing (RS) data, deriving RS-based features, or producing RS-based analytical products (e.g. digital soil mapping).
  • Experience working with process-based biogeochemical models (e.g. Century, DNDC, SALUS, RothC, Ecosys, etc.).
  • Experience with machine learning or other advanced statistical techniques.
  • Strong spatial data analysis skills (e.g. Python, GDAL, SQL, vector/raster data).
  • Experience conducting model validations for use with soil carbon crediting protocols.
  • Prior industry experience in regenerative agriculture.
  • Strong, active presence in the scientific community.

You’ll love working at Perennial because:

  • We live by our Core Values.
    • Speak your truths, welcome new voices.
    • Celebrate your successes, own your mistakes.
    • Solve important problems.
    • Invest in each other.
    • Build for the future.
    • Get your hands dirty!
  • We challenge the status quo. We’re a group of people who want to create the changes we hope to see in the world. See some of our recent press about the problems we’re committed to solving. 
  • We invest in your life. We want to provide you with resources to meet your needs both in and outside of work. We offer generous PTO, health, vision, dental, 401k, and HSA benefits and a fully stocked kitchen to keep your mind sharp throughout the day.
  • We want you to grow. We are a team that supports each others’ professional and intellectual growth. You’ll have access to regular opportunities to learn from teammates and invest in your professional development.
  • We offer competitive compensation packages. Our team is our most valuable asset. We want everyone who works for us to feel fairly compensated for the impact they bring to our mission. The team member in this role can expect a starting salary in the range of $100,000-$160,000 alongside equity in the company.
  • Perennial is an equal opportunity employer. We celebrate and embrace diversity and are committed to building a team that represents a variety of experiences, backgrounds, and skills. We do not discriminate on the basis of race, color, religion, marital status, age, gender identity, gender expression, sexual orientation, non-disqualifying physical or mental disability, national origin, veteran status, or other applicable legally protected characteristics.



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