Soil Carbon Modeler at Agoro

Chicago, Illinois, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Apr, 26

Salary

0.0

Posted On

18 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Soil Science, Agronomy, Biogeochemical Modeling, Data Science, Model Calibration, Model Validation, Uncertainty Analysis, Python, R, Ecosystem Models, Carbon Crediting, Regulatory Reporting, Scientific Rigor, Team Collaboration, Technical Communication

Industry

Farming

Description

Agoro Carbon Alliance addresses a key global problem: the impact of agriculture's global greenhouse gas emissions on our climate and the implementation of sustainable carbon-capturing potential on ranches and farms. Our passion lies in solving a global crisis by working with our customers to bring about the sustainable transformation of ranching and farming practices that are economically viable for the grower and help the world avoid a crisis. A bit about the role: Our team brings together soil science, agronomy, biogeochemical modeling, and applied data science to develop and operate Agoro Carbon’s carbon-ready modeling pipeline for project-level MRV, uncertainty analysis, and credit quantification across large-scale agricultural and rangeland systems. As a soil carbon modeler, you will contribute to reviewing the existing modeling approaches circulated in the scientific community and nature-based soil carbon removal industry to design, customize, and continuously evolve Agoro Carbon’s production-grade soil carbon modeling framework used for project certification, uncertainty quantification, and regulatory reporting. You will own and influence: The scientific integrity and evolution of Agoro Carbon’s core soil carbon modeling framework Calibration, validation, and uncertainty methodologies used for credit issuance Integration of new regenerative practices into production MRV pipelines Scientific defensibility for auditors, registries, and regulatory stakeholders What you will be doing Build, calibrate, validate, and uncertainty-quantify large-scale soil carbon simulations for agricultural and rangeland systems Design and implement new algorithms and structural model enhancements to improve the representation of regenerative practices Contribute to production-ready scientific pipelines in partnership with Grower Success Team, Data Team, and Product/Tech teams Perform ensemble calibration, Monte Carlo uncertainty analysis, and regulatory-grade reporting Evaluate model behavior against empirical soil datasets and peer- reviewed literature What will you bring? PhD in agriculture, soil science, or forestry Minimum of 5 years’ experience working on ecosystem models for biogeochemical modeling in industry. Expertise in working with at least three soil carbon models (e.g., DayCent, DNDC, APEX, or SWAT-C ). Highly proficient and experienced in scripting languages such as Python and/or R, with the ability to write clean, shareable, and efficient code. Ability to work effectively as part of a team and communicate technical concepts to stakeholders with or without a technical background. What will set you apart: Deep expertise in process-based ecosystem models and their parameterization Experience publishing or defending models in peer-reviewed or regulatory settings Strong understanding of MRV, carbon crediting, and uncertainty frameworks Ability to translate scientific rigor into production systems Comfort owning high-impact modeling decisions Why work with us? We offer the opportunity to drive change by globally reducing carbon emissions while financially supporting growers. You would be working with a globally dispersed and diverse team. We adopt a virtual-first approach, where we encourage face-to-face collaboration, but are focused on recruiting the best talent. Continuous learning, research engagement, and professional development are actively supported.

Responsibilities

As a Soil Carbon Modeler, you will build, calibrate, validate, and quantify uncertainty in large-scale soil carbon simulations for agricultural and rangeland systems. You will also design and implement new algorithms to enhance the modeling framework.