Agronomic Systems Modeling Specialist

Location: Johnston, Iowa
Pay Rate: $55 per hour
Assignment Length: 12 months
Work Arrangement: Hybrid, onsite, or fully remote depending on candidate qualifications
Preferred Work Location: John Deere Factory (JDF); John Deere Cary will also be considered
Remote Schedule: Approximately 8:00 a.m.–5:00 p.m. Central Time
Travel: Ability to travel to the Des Moines area approximately one to two times per year

Position Overview

We are seeking an Agronomic Systems Modeling Specialist to develop and apply process-based crop models that support agronomic decision-making across diverse production environments. This role will focus on modeling genotype-by-environment-by-management interactions and translating complex agricultural data into practical, scientifically defensible recommendations.

The ideal candidate will have advanced experience with crop modeling—particularly in corn or soybean systems—combined with strong skills in agronomy, data science, geospatial analysis, and reproducible analytical workflows. A Ph.D. related to process-based crop modeling and experience in the agricultural or seeding industry are highly preferred.

Key Responsibilities

• Develop modeling approaches for genotype × environment × management (G × E × M) interactions.
• Predict outcomes of management scenarios, including:
  • Planting date
  • Variety selection
  • Fertility management
  • Crop care
  • Other agronomic practices
• Apply models across corn, soybean, and cotton production systems and diverse geographies.
• Design and execute model calibration, validation, and sensitivity analyses.
• Quantify model performance, uncertainty, and limitations across years, locations, environments, and management systems.
• Define modeling problems, required inputs, expected outputs, assumptions, and validation criteria.
• Analyze large, machine-generated agricultural datasets, including planter, sprayer, and harvest data.
• Work with geospatial datasets such as:
  • Soil maps
  • Topography
  • Multispectral imagery
  • Remote sensing products
  • Environmental data layers
• Develop repeatable workflows for processing, summarizing, analyzing, and visualizing model outcomes.
• Develop agronomic logic, constraints, and validation frameworks to 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 model outcomes.
• Translate research findings and scientific models into practical decision-support tools, grower recommendations, and operational workflows.
• Communicate model assumptions, results, limitations, and uncertainty to both technical and nontechnical audiences.

Required Qualifications

• Experience developing, calibrating, validating, and applying APSIM, DSSAT, or comparable process-based crop models for agricultural decision support.
• Strong understanding of:
  • Crop physiology
  • Crop phenology
  • Soil water dynamics
  • Nutrient cycling
  • Crop responses to management and environmental conditions
• Strong understanding of at least one major U.S. row-crop production system.
• Expertise in corn and soybean production is preferred.
• Advanced proficiency in R and/or Python for data analysis, simulation workflows, and model development.
• Experience working with Databricks, SQL, cloud computing environments, and APIs to support scalable analytical and simulation workflows.
• Demonstrated experience with AI-assisted development.
• Experience working with large, multi-environment, multi-year, or multi-management agricultural datasets.
• Experience developing reproducible analytical workflows.
• Ability to explain complex modeling concepts, assumptions, results, limitations, and uncertainty clearly.
• Experience translating scientific models or research outputs into practical decision-support tools, recommendations, or operational processes.
• Master’s or Ph.D. in one of the following areas:
  • Agronomy
  • Crop Science
  • Soil Science
  • Biological Systems Engineering
  • Agricultural Engineering
  • Quantitative Genetics
  • A closely related discipline

Preferred Qualifications

• Ph.D. related to process-based crop modeling.
• Expertise in corn or soybean systems.
• Professional experience in the agricultural, agronomic technology, or seeding industry.
• Experience with John Deere Operations Center.
• Familiarity with precision agriculture technologies, including:
  • Planting
  • Spraying
  • Harvest
  • Automation
  • Sensing
  • Variable-rate management systems
• Experience working with machine-generated agricultural data.
• Familiarity with grower-facing agronomic data layers, including:
  • Field boundaries
  • Management zones
  • Digital elevation models
• Experience developing agronomic models for the agricultural industry.
• Experience creating agronomic constraints or validation systems for AI-generated recommendations.
• Experience integrating drone and satellite data into analytics pipelines.
• Proficiency with SQL, R, Python, Tableau, Power BI, or similar tools.