• Duration: 1+ Years Contract
• Location: Johnston, IA/ Cary NC -Remote

• Position Summary

  We are seeking a highly skilled Senior Crop Modeling Scientist / Agricultural Systems Modeler to lead the development, validation, and application of advanced agricultural models that support decision-making across diverse crop production systems. This role will focus on predicting crop performance under varying genetic, environmental, and management conditions while leveraging large-scale agronomic, geospatial, and machine-generated datasets.

  The successful candidate will contribute to engineering and scientific initiatives related to crop production, process improvement, data-driven modeling, and agricultural innovation. This position operates within a collaborative, multidisciplinary environment and works closely with agronomists, data scientists, software engineers, and product teams.

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  Key Responsibilities

  Engineering & Process Development

  • Analyze production systems, processes, equipment, and methodologies to support the design and continuous improvement of agricultural and manufacturing-related operations.
  • Contribute to the development, testing, and validation of engineering and scientific solutions.
  • Support the creation and evaluation of technical standards, specifications, and best practices.
  • Participate in quality assessments, inspections, and compliance activities and provide recommendations for process improvements.
  • Identify operational inefficiencies and implement improvements using established change management practices.
  • Execute project objectives within established project plans and timelines.

  Crop Modeling & Analytics

  • Develop crop modeling approaches to quantify Genotype × Environment × Management (G×E×M) interactions across major row-crop production systems.
  • Design and conduct model calibration, validation, sensitivity analysis, and performance assessments.
  • Evaluate relationships among genetics, weather, soil conditions, crop management practices, and cropping history.
  • Develop predictive models for management decisions including planting strategies, hybrid/variety selection, fertility programs, crop protection, and production optimization.
  • Utilize large-scale agricultural datasets, including planting, spraying, and harvest operation data.
  • Work extensively with geospatial and environmental datasets such as soil maps, terrain information, remote sensing products, and satellite imagery.
  • Build scalable workflows for data processing, simulation, visualization, and decision-support systems.
  • Develop scientific validation frameworks and agronomic constraints to improve the reliability and transparency of AI-assisted recommendations.
  • Communicate modeling outcomes, assumptions, uncertainties, and recommendations to both technical and non-technical stakeholders.

  Collaboration & Professional Development

  • Partner with cross-functional teams including agronomy, research, data science, software engineering, and product management.
  • Provide technical guidance on modeling methodologies, engineering solutions, and emerging technologies.
  • Maintain awareness of industry trends, scientific advancements, regulations, and best practices through professional development activities.

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  Required Qualifications

  • Master's or Ph.D. in Agronomy, Crop Science, Soil Science, Agricultural Engineering, Biological Systems Engineering, Quantitative Genetics, or a related discipline.
  • Strong experience developing, calibrating, validating, and applying APSIM, DSSAT, or similar process-based crop models.
  • Deep understanding of crop physiology, phenology, soil-water relationships, nutrient dynamics, and crop responses to environmental and management factors.
  • Expertise in at least one major U.S. row-crop production system; corn and soybean experience strongly preferred.
  • Advanced proficiency in Python and/or R for modeling, simulation, and analytics.
  • Experience working with SQL, Databricks, cloud platforms, APIs, and large-scale analytical workflows.
  • Experience utilizing AI-assisted development tools and methodologies.
  • Proven ability to manage and analyze large multi-year, multi-location agricultural datasets.
  • Strong communication skills and the ability to translate scientific research into actionable business recommendations.

Medical, dental, and vision insurance are available to qualified candidates who meet eligibility requirements.

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