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 an initiative to scale Enhanced Rock Weathering within the next decade, starting in Brazil.

Founded out of the Stanford University ecosystem, Terradot brings together expertise from industry, academia, and government to advance the science and technology of Enhanced Rock Weathering.

Terradot has raised $58.2 million in funding from investors including John Doerr, Sheryl Sandberg and Tom Bernthal, George Roberts, Microsoft’s Climate Innovation Fund, Google, Cisco, Floodgate, Kleiner Perkins, Acre Venture Partners, Gigascale Capital, Valor Capital, Ponderosa Ventures, and others. We have sold approximately 300,000 tons in offtakes to carbon removal buyers including 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

About the Role

Terradot is seeking an early-career Data Scientist to develop geospatial models, statistical analyses, and quantitative products that support the measurement and scaling of our Enhanced Rock Weathering operations.

You will work with spatial, temporal, field, laboratory, remote sensing, and operational data to answer applied scientific and business questions. Your work may include exploratory analysis, model development, prediction, imputation, uncertainty estimation, experimental design, and the creation of reusable analytical workflows.

This role is designed for candidates who have developed strong quantitative foundations through a master’s degree and relevant applied experience, a second or third professional role in data science or a related field, or a PhD followed by a first or second industry role.

You do not need to arrive as an expert in geochemistry, carbon removal, or agriculture. You should be excited to learn the scientific domain, work closely with subject-matter experts, and translate complex data into rigorous and useful conclusions.

You will collaborate with scientists, data engineers, software engineers, and operational teams. Engineers will primarily own ingestion, orchestration, and warehouse infrastructure, while you will own geospatial and statistical analyses built on top of those systems.

What You’ll Do

• Develop geospatial and statistical models using field, laboratory, remote sensing, environmental, and operational data.
• Analyze spatial and temporal variation across Terradot’s project areas and operating regions.
• Build models for prediction, interpolation, imputation, classification, estimation, and inference.
• Quantify model uncertainty and communicate the assumptions, limitations, and practical implications of analytical results.
• Design and evaluate statistically rigorous approaches for measuring carbon removal and related environmental outcomes.
• Translate scientific questions into clear analytical plans, datasets, models, and decision-ready outputs.
• Partner with scientists to translate domain knowledge into model features, assumptions, constraints, and validation strategies.
• Produce maps, visualizations, reports, and analytical tools that make complex results accessible to technical and nontechnical stakeholders.
• Contribute to code review, testing, documentation, and shared standards for quantitative work.

What We’re Looking For

• A master’s or PhD in statistics, data science, computer science, applied mathematics, geospatial science, environmental science, engineering, another quantitative discipline, or equivalent applied experience.
• Strong foundations in statistics, including concepts such as sampling, regression, uncertainty, model validation, and experimental or observational study design.
• Strong programming skills in Python and experience using SQL to access and analyze structured data.
• Experience developing and evaluating statistical or machine learning models using real-world data.
• Experience communicating technical methods and results through clear writing, visualizations, and presentations.
• Interest in collaborating closely with scientists, engineers, and operational teams.
• Fluent English communication skills
• Comfort receiving feedback and revising analyses as new data or domain understanding becomes available.

We value analytical judgment, scientific curiosity, and demonstrated learning ability more than experience with any specific modeling library or environmental domain.

Nice to Have

• Experience with geospatial Python tools such as GeoPandas, rasterio, xarray, rioxarray, shapely, or PySAL.
• Familiarity with spatial databases and formats, including PostGIS, GeoTIFF, Zarr, or STAC.
• Experience with remote sensing, satellite imagery, public gridded datasets, or environmental monitoring data.
• Experience with spatial statistics, geostatistics, Bayesian modeling, causal inference, time-series analysis, or hierarchical models.
• Familiarity with agricultural systems, soil science, geochemistry, climate science, ecology, or carbon accounting.
• Experience deploying models or integrating analytical code into production workflows.
• Familiarity with cloud platforms such as AWS or GCP.
• Experience collaborating through shared repositories, code review, automated testing, and version-controlled analytical projects.

Personal Attributes

• Intellectually curious and motivated to understand both the data, the underlying physical systems, and the end to end chains of data production.
• Practical and focused on producing work that supports real decisions.
• Comfortable moving between detailed technical analysis and clear stakeholder communication.
• Collaborative, receptive to feedback, and willing to learn from domain experts.
• Organized and thoughtful about reproducibility, documentation, and analytical quality.
• Positive and action-oriented, with a willingness to take ownership of problems.