Agronomic Systems Modeling Specialist

Title - Agronomic Systems Modeling SpecialistJob Duration - 01 years (Possible Extension)Consider fully remote. If remote, would need to be available roughly 8-5 Central time. Would also need to be able to travel to Des Moines 1 or 2 times pers year.Ideally looking for someone with a PhD (related to process based crop modeling (expert in Corn or Soy)) and some industry experience (Ag or seeding companies).Responsibilities

• Develop modeling approaches for G x E x M interactions to predict the outcomes of various management scenarios (planting date, variety selection, fertility management, crop care, etc.) in corn, soybean and cotton production systems across diverse geographies.
• Design and execute model calibration, validation, and sensitivity analyses to quantify model performance, uncertainty, and limitations across geographies, years, and management systems.
• Understand interactions among genotype, weather, soil, management, and cropping history to clearly define modeling problems, input requirements, outputs, assumptions, and validation criteria.
• Work with large, machine-generated agricultural datasets including planter, sprayer, harvest data.
• Work with large geospatial datasets including soil maps, topography, multispectral imagery, remote sensing products, and environmental data layers.
• Develop repeatable workflows for processing, summarizing, and visualizing outcomes of these models.
• Develop agronomic logic, constraints, and validation frameworks that ensure AI-generated recommendations are agronomically sound, transparent, and scientifically defensible.
• Collaborate with agronomists, data scientists, software developers, and product managers to build, scale, and communicate outcomes of these models.Required Qualifications
• Exp. developing, calibrating, validating, and applying APSIM, DSSAT, or comparable process-based crop models for agricultural decision support.
• Strong understanding of crop physiology, phenology, soil water dynamics, nutrient cycling, and their influence on crop response to management and environment.
• Strong understanding of at least one major US row crop production system, with expertise in corn and soybean production preferred.
• Advanced proficiency in R and/or Python for data analysis, simulation workflows, and model development.
• Exp. working with Databricks, SQL, cloud computing environments, APIs, for scalable analytical and simulation workflows.
• Demonstrated experience with AI assisted development.
• Experience working with large multi-environment, multi-year, or multi-management agricultural datasets and developing reproducible analytical workflows.
• Ability to communicate model assumptions, results, limitations, and uncertainty to both technical and nontechnical audiences.
• Experience translating scientific models or research outputs into practical decision-support tools, recommendations, or operational workflows for growers.
• Master’s or Ph.D. in agronomy, crop science, soil science, biological systems engineering, agricultural engineering, quantitative genetics, or a closely related discipline.Preferred Qualifications
• Experience with precision agriculture technologies including planting, spraying, harvest, automation, sensing, and variable-rate management systems.
• Familiarity with machine-generated datasets and common grower-facing agronomic data layers (field boundaries, management zones, digital elevation models).
• Experience working in agricultural industry with agronomic model development.
• Experience developing agronomic constraints or validation systems for AI-generated recommendations.
• Experience integrating drone and satellite data into analytics pipelines.
• Proficiency in SQL, R, Python, Tableau, Power BI, or similar tool