Become a
Groundworker

What you’ll do
• Ingest sampling-campaign data (field records, chain-of-custody, lab results) into the program database and normalize it across labs and vendors
• Run QC checks on soil data (BD, TOC, SOC, MAOM), track down anomalies with field teams and labs, and document how each one is resolved
• Run registry calculation pipelines (baseline, removals, emissions) and prepare the evidence packages for monitoring reports and verification
• Turn methodology into working, tested code, validate it against reference cases and extend it as requirements change
• Analyze monitoring results across regions, crops and management practices to explain why outcomes vary in the field
• Build and maintain dashboards and recurring reports for the science, operations and commercial teams
• Write reusable Python/SQL tools that cut manual work

What you bring
• MSc in Data Science, Statistics, Computer Science or a related quantitative field
• 2+ years of relevant professional experience
• Solid applied statistics (variance and uncertainty, sampling, hypothesis testing, regression) and experience with noisy field or lab data
• Proficiency in Python (pandas, numpy) and SQL, with clean, documented, reproducible code
• Proven experience implementing analytical algorithms as tested, maintainable software
• Practical use of AI tools to speed up data work, and the judgment to verify what they produce
• High attention to detail
• Strong written and spoken English

Nice to have
• Background in soil carbon, agronomic field trials or GHG accounting
• Familiarity with Verra VM0042 or other carbon-market MRV standards
• Git, database design or data-pipeline tooling
• GIS / Earth Engine, or Streamlit / Dash
• Hebrew

What you’ll do
• Review statistical and data-science approaches before the team commits to them
• Advise on the models behind soil-carbon quantification, including our process-based soil–plant–mycorrhiza model (calibration, validation and uncertainty)
• Guide Monte Carlo design and uncertainty quantification, including the uncertainty deductions applied to credit issuance
• Advise on soil sampling plans, control-plot design and responses to verifier findings
• Help define QA/QC procedures for field, lab and remote-sensing data
• Support the team in technical discussions with rating agencies, verifiers and modeling partners

What you bring
• Ph.D. in statistics, applied mathematics or a quantitative science with a strong statistical component
• 10+ years of hands-on experience applying statistics and mathematical modeling to physically measured, real-world data
• Strong foundations in probability, inferential statistics, linear algebra and optimization
• Proven experience with Monte Carlo simulation and uncertainty quantification
• Experience designing sampling schemes and experiments (stratified sampling, control/treatment designs)
• Proficiency in R and/or Python for statistical computing
• Ability to defend methodology to external reviewers, auditors or regulators
• Excellent written and spoken English

Advantages
• Background in soil science, soil carbon or agronomy (or else biology, physics or chemistry)
• Experience calibrating and validating process-based models, or working with spatial and remote-sensing data
• Familiarity with carbon markets and MRV (Verra VM0042, insetting, Scope 3 / SBTi FLAG)
• Prior work with rating agencies, verification bodies or scientific peer review
Deep learning, LLMs and MLOps are not required.

Engagement: long-term, consultant or part-time contract · Start ASAP · Occasional calls across US, Brazil and European time zones
To apply: send your CV and a short note describing a project where you built a statistical or simulation model for real-world measured data and defended it to an external reviewer.