Carbon and GHG modeler

Varaha is hiring a Carbon and GHG modeler to advance the modeling science behind our agricultural carbon projects. We are looking for someone who has taken a Model Validation Report (MVR) from start to finish and can bring that end-to-end experience to a function ready to scale.

You will lead model calibration, validation, and uncertainty quantification for Varaha's VM0042 and equivalent projects; defend the modeling approach to VVBs and third-party evaluators; and build production modeling pipelines that scale across geographies. You will work closely with the Modeling Lead, the Carbon Project Lead, and the broader Science team.

What you will own

• VMD0053-compliant calibration, validation, and uncertainty quantification for VM0042 projects, including structural validation and project-specific back-modeling.
• Authorship of Model Validation Reports (MVRs) and direct engagement with VVBs and third-party evaluators on model-specific Corrective Action Requests, Clarification Requests, and reviewer queries.
• Production modeling pipelines in Python - scalable across geographies, version-controlled, and reproducible - not one-off scripts.
• Scientific defense of model selection, parameterization, and uncertainty treatment in PDDs, MRs, and rebuttals to third-party evaluations.
• Contributing to the technical bar of the modeling function as the team grows.

What we're looking for

• Demonstrated experience authoring or co-authoring a complete Model Validation Report from calibration through submission under Verra, Gold Standard, or comparable registry.
• Hands-on calibration and parameterization with DayCent, DNDC, DSSAT, or comparable process-based biogeochemical models.
• Working knowledge of Verra VM0042 and VMD0053, including the distinction between structural validation and project-specific back-modeling.
• Production-grade Python, Git, and geospatial data handling — not notebook-only.
• M.S. or Ph.D. in soil science, agronomy, biogeochemistry, environmental science, or a quantitative equivalent.

Preferred

• Prior VVB interaction during validation or verification cycles.
• Bayesian calibration (PyMC, PEST, or similar) applied to soil carbon or GHG models.
• Experience with Indian, Southeast Asian, or Sub-Saharan African smallholder cropping systems.
• ML surrogates for process-based models; remote sensing (Sentinel-2, MODIS, soil property rasters).
• Experience integrating LLM-assisted workflows into model documentation, calibration data extraction, or MVR development.

We hire for strategic alignment and cultural fit. If you bring exceptional capability with slightly more or less experience than the band above, we'd still like to hear from you.