Senior Data Scientist; Geospatial

We are seeking a cross-functional Senior Data Scientist (Geospatial) to lead the development and deployment of scalable geospatial solutions that drive critical business decisions. In this role, you will harness the power of spatial statistics, GIS, and machine learning to optimize field operations, enhance data accessibility, and support large-scale decision-making. This is an individual contributor (IC) role with high autonomy and the opportunity to simultaneously shape the company’s geospatial data stack and capabilities and contribute to foundational modeling strategies.

Location: San Francisco, CA or São Paulo, Brazil

Languages: English required, Portuguese a plus

Role Responsibilities:

• Develop and product ionize scalable spatial statistical measurement designs for our field teams to execute in our MRV (Measure, Report, and Verify) platform.

• Design and implement robust geospatial models for prediction of soil, plant, and water measures, imputation of missing data, and prediction of environmental outcomes.

• Architect and manage spatial data storage solutions for performant storage and recovery of national-scale datasets.

• Work at the cutting edge of research in ERW spatial sampling techniques to develop statistically rigorous decision-making and credit-generating frameworks.

• Collaborate with engineering teams on core geospatial infrastructure, model deployment, and decision-support products.

Background and Requirements:

• 3+ years of industry experience in geospatial data science
  , spatial statistics, or applied machine learning with a strong emphasis on geospatial applications.

• Deep expertise in managing geographic data in an RDBMS (PostGIS, etc), Python (Geo Pandas, shapely, etc), and file storage (Geo Arrow, Geo Parquet, etc).

• Hands-on experience with spatial datasets, geospatial modeling, and large-scale data processing
  .

• Strong knowledge of spatial statistics, sampling methodologies, and experimental design
  .

• PhD in a quantitative field (Statistics, Machine Learning, or related) preferred
  , or equivalent industry experience.

Nice to Haves:

• Experience in agriculture, environmental science, climate tech, or remote sensing
  .

• Familiarity with environmental data, remote sensing, and public gridded environmental data.

• Familiarity with API integrations and data retrieval.

• Familiarity with methods for statistical monitoring of natural resources.

Personal Attributes:

• Strategic thinker who can balance short-term needs with long-term vision.

• Highly collaborative, with strong leadership skills and a hands-on approach to problem-solving.

• Ability to navigate ambiguity and prioritize effectively in a fast-paced environment.

• A positive, action-oriented mindset with a focus on outcomes.

What does success look like in this role?

Meaningful contributions to the advancement of our internal technical stack and methodology development, and providing support to the Sci Ops team to execute on our ambitions.

About Terradot:

Our mission is to stabilize Earth’s climate by transforming nature’s most powerful permanent carbon removal process into a global climate solution. By advancing science, building technology, and assembling a global coalition, we are catalyzing a global initiative to scale Enhanced Rock Weathering within the next decade, starting in Brazil.

Founded out of the Stanford University ecosystem, Terradot is led by the world’s leading experts to advance the science and technology of ERW. Our unique structure bridges industry, academia, and government and allows our team to contribute with speed & scale. We have raised $58.2M in total funding from John Doerr, Sheryl Sandberg & Tom Bernthal, George Roberts, Microsoft’s Climate Innovation Fund, Google, Cisco and Venture Funds, Floodgate, Kleiner Perkins, Acre Venture Partners, Gigascale Capital, Valor Capital, Ponderosa Ventures and others.

We have sold ~300,000 tons in offtakes from leading CDR buyers like Frontier and Google.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other characteristic protected by law. Research suggests that qualified people from historically marginalized groups may self-select out of opportunities if they don't meet 100% of the job requirements. We encourage individuals who believe they have the skills necessary to thrive to apply for this role.