> Markdown version of [/jobs/ext/3096095-data-soil-science](https://www.wearedevelopers.com/jobs/ext/3096095-data-soil-science). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data & Soil Science - **Company:** GRASSROOTS CARBON CAPTURE INC. - **Location:** San Antonio, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Python (Programming Language), Micro Electro-Mechanical Systems (MEMS), Spatial Data Infrastructures, Data Processing, Model Validation, Data Assimilation, Docker - **Published:** September 26, 2026 - **Apply:** https://www.thejobnetwork.com/job/e2282330-701a-482e-8e42-adf811a8dcd4/senior-biometrician-carbon-quantification ## About the Role 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., * 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. Preferred Skills * 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. ## Description * 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 timepoints. * 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 implement consistent statistical methods and reproducible workflows. Establish documented procedures for data screening, estimation, validation, and uncertainty reporting. * Technical Documentation and Review: 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. * Data Collection Priorities: Quantify the expected benefits and costs of additional cores, repeat visits, laboratory replicates, and environmental monitoring. 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