Senior Biometrician Carbon Quantification

Team
:
Data & Soil Science |

Reports to:

VP, Data & Soil Science Location
:
San Antonio, TX | Travel: 10–15% | Type:
Full-time, Individual Contributor

Grassroots Carbon partners with ranching families to strengthen the economics and long-term sustainability of working lands through regenerative grazing practices that improve soil health, increase forage productivity, enhance water cycles, restore grasslands, support wildlife habitat, and build resilience to drought and extreme weather. In doing so, ranchers unlock new revenue streams while preserving their heritage and strengthening rural communities.

Today, Grassroots Carbon partners with over 300 ranching families across more than 2.5 million acres in 22 states, making us the largest grassland soil carbon developer in the United States. Through this work, we have delivered more than 1.9 million verified carbon removals while helping ranchers generate measurable land stewardship outcomes across America's working landscapes.

Grassroots Carbon is trusted by leading corporate partners including Nestlé, Microsoft, Shopify, Olipop, Chevron, and Boeing. We collaborate closely with organizations including Audubon Conservation Ranching, Texas Agricultural Land Trust, and the Colorado State University Soil Carbon Solutions Center to ensure scientific rigor, transparency, and environmental outcomes at scale.

We are looking for a biometrician, spatial statistician, or quantitative ecologist with strong applied judgment and experience working with imperfect environmental data. You will develop and evaluate methods for estimating soil carbon stock change, combining field measurements, spatial information, and process models while accounting for uncertainty. This work requires independent thinking, careful testing of assumptions, and the ability to turn unresolved questions into practical analyses and targeted data collection.

Bayesian hierarchical modeling and continuous monitoring will be important parts of the role as we connect repeated soil measurements with environmental observations over time. You should be comfortable developing new approaches, explaining their limitations, and revising them as the evidence changes.

• Sampling and Estimation:
  Design sampling and repeat-measurement programs for estimating carbon stocks and stock change at point, ranch, and portfolio scales. Develop design-based, model-assisted, and model-based estimators appropriate to the sampling design, with explicit treatment of area weighting, spatial dependence, missing observations, and minimum detectable change.

• Measurement Quality and Comparability:
  Develop methods to distinguish ecological change from sampling, laboratory, and data-processing effects. Investigate repeat-location alignment, core recovery, coarse fragments, organic and inorganic carbon measurements, and differences between laboratories or analytical methods. Establish reproducible quality controls and design targeted reanalysis or resampling to resolve consequential uncertainties.
• Uncertainty Quantification:
  Develop hierarchical statistical models and propagate uncertainty from field sampling and laboratory measurements through equivalent-soil-mass stock calculations, modeled change, and reported or credited quantities. Account for measurement error, systematic bias, shared sources of error, and dependence across locations, depths, and time points.

• Model Evaluation:
  Design independent tests of soil carbon and spatial prediction models, including benchmarks, validation across sites and time periods, and sensitivity to initialization, inputs, and measurement uncertainty. Evaluate bias, predictive accuracy, and uncertainty coverage, and document the conditions under which each model is suitable for use.

• Continuous Monitoring and Data Assimilation:
  Develop and evaluate Bayesian hierarchical, state-space, and data-assimilation methods that combine repeated soil measurements with process models, remote sensing, flux-tower observations, and environmental monitoring. Work with soil scientists, modelers, and remote sensing specialists to estimate changing ecosystem states and their uncertainty. Maintain clear separation between calibration and independent validation and establish when monitoring updates are sufficiently supported for operational decisions, reporting, or crediting.

• Statistical Methods in Practice:
  Partner with software engineers, modelers, laboratory partners, and field operators to…