Senior Biometrician Carbon Quantification

Grassroots Carbon • San Antonio, TX

6d • Onsite

About The Position

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.

Requirements

• A PhD, or an MS with an equivalent applied track record, in statistics, biostatistics, geostatistics, applied mathematics, or quantitative ecosystem science.
• Proven experience developing, documenting, and evaluating statistical methods through external regulatory, audit, or peer review.
• Strong applied experience with Bayesian hierarchical modeling, including model checking, uncertainty quantification, and sensitivity to assumptions. Familiarity with tools such as Stan, PyMC, or NumPyro.
• Practical experience with spatial sampling, repeated-measures inference, measurement-error analysis, and uncertainty propagation in heterogeneous environmental systems.
• Experience selecting and applying design-based, model-assisted, or model-based estimation, with an understanding of the assumptions and limitations of each.
• Strong R or Python skills, reproducible and versioned analytical workflows, and the ability to contribute to a Python-based production environment.
• The ability to communicate methods, evidence, and limitations clearly to scientific peers, field teams, executives, and external reviewers.

Nice To Haves

• Experience with state-space models, sequential inference, or data assimilation for continuous environmental monitoring.
• Experience with laboratory method comparisons, soil measurements, survey sampling, or long-term environmental monitoring programs.
• Experience integrating digital soil maps, remote sensing, or process-model predictions into model-assisted estimators while preserving independent validation.
• Familiarity with carbon crediting and greenhouse gas accounting frameworks, such as Verra VM0042, Isometric, CAR, or GHG Protocol.
• Familiarity with soil carbon or agroecosystem models such as RothC, DayCent, MEMS, or DNDC, or with eddy covariance observations.
• Comfort with spatial data tools such as xarray and GeoPandas, and cloud or Docker environments.

Responsibilities

• 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.
• 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.
• 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 timepoints.
• 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.
• 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.
• Partner with software engineers, modelers, laboratory partners, and field operators to implement consistent statistical methods and reproducible workflows.
• Establish documented procedures for data screening, estimation, validation, and uncertainty reporting.
• Lead the statistical components of technical review with registries, verification bodies, and buyer diligence teams.
• Write clear methods and uncertainty documentation, explain assumptions and limitations, and support evaluations under applicable requirements, including Verra and Isometric standards.
• Quantify the expected benefits and costs of additional cores, repeat visits, laboratory replicates, and environmental monitoring.
• Recommend investments that reduce consequential uncertainty and help distinguish competing explanations for model–measurement disagreement.

Benefits

• Health Insurance ($0 co-pay and $0 deductible)
• Dental, and vision insurance plans, including flexible spending account options
• Open Paid Time Off Policy plus company holidays as outlined in our handbook
• Participation in our 401(k) savings plan
• Company-paid Life and AD&D coverage
• Educational materials and expenses supporting continuing education opportunities

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree