Crop Modeling Scientist/Agricultural Modeling Scientist Location: Remote

Job Title: Crop Modeling Scientist/Agricultural Modeling Scientist

Location: RemoteContract: 12 months may extendPR: $50/hr Job PurposePosition: Crop Modeling Scientist / Agronomic Modeling Scientist

Location: Des Moines, IA or Fully RemoteRemote: Yes – must be available approximately 8 AM–5 PM Central Time and travel to Des Moines 1–2 times per yearRole SummaryLooking for a Crop Modeling/Agronomy Scientist with strong experience in process-based crop modeling, ideally with a PhD in Agronomy, Crop Science, Soil Science, Agricultural Engineering, or related field.The ideal candidate will have hands-on experience building, calibrating, and validating crop models using real-world agricultural data, with expertise in corn and/or soybean.

Industry experience with an agricultural, seeding, or precision-ag company is highly preferred.Key ResponsibilitiesDevelop process-based models for G × E × M (Genotype × Environment × Management) interactions.Model management scenarios such as planting dates, varieties, fertility, and crop care.Calibrate, validate, and perform sensitivity/uncertainty analysis on crop models.Analyze interactions between genetics, weather, soil, management, and cropping history.Work with large agricultural datasets from planting, spraying, and harvesting equipment.Analyze geospatial data including soil maps, topography, satellite/drone imagery, and environmental layers.Build reproducible workflows for model processing, analysis, visualization, and simulation.Develop agronomic rules and validation frameworks for AI-generated recommendations.Collaborate closely with Agronomists, Data Scientists/Engineers, Software Developers, and Product Managers.Translate scientific modeling results into practical grower decision-support tools.Must-Have QualificationsMaster’s or PhD in Agronomy, Crop Science, Soil Science, Agricultural Engineering, Biological Systems Engineering, Quantitative Genetics, or related field.Hands-on experience with APSIM, DSSAT, or comparable process-based crop models.Experience building + calibrating + validating models using real-world data.Strong knowledge of crop physiology, phenology, soil water dynamics, and nutrient cycling.Strong knowledge of corn and/or soybean production systems.Advanced R and/or Python skills.Experience with large, multi-year/multi-environment agricultural datasets.Experience with Databricks, SQL, cloud platforms, and APIs.Experience with AI-assisted development.Ability to communicate complex modeling concepts, assumptions, limitations, and uncertainty to both technical and non-technical teams.Ability to translate research/models into practical agricultural decision-support solutions.PreferredAgricultural industry experience, especially ag-tech, seeding, precision agriculture, or agronomic modeling.John Deere Operations Center experience.Precision agriculture technologies: planting, spraying, harvesting, sensing, automation, variable-rate management.Experience with machine-generated farm/equipment data.Experience with field boundaries, management zones, DEM/topography, drone/satellite data.Experience developing agronomic constraints/validation frameworks for AI recommendations.Tableau, Power BI, or similar visualization tools.Strong farming/agricultural background.Candidate Profile to TargetBest fit: PhD + process-based crop modeling + hands-on calibration/validation + corn/soy expertise + Python/R + real agricultural datasets + ability to work cross-functionally.Also consider: Recent PhD graduates if they have a highly relevant internship/research project involving real-world crop model calibration/validation.Key screening point: Don't focus only on candidates who have built crop models. The critical differentiator is experience calibrating and validating models against real-world agricultural data