Agronomic Systems Modeling Specialist

VIVA USA Inc; Johnston (IA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

VIVA USA Inc. is seeking a highly skilled agronomic data scientist to develop Genotype x Environment x Management models for corn, soybean, and cotton production.

You will leverage large datasets from connected equipment, weather, soils maps, satellite and drone imagery to build scalable agronomic decision-support tools for internal stakeholders and farmers. The ideal candidate holds a PhD related to crop modeling and has extensive experience with APSIM/DSSAT, plus proficiency in R or Python.

Qualifications

• PhD related to process-based crop modeling (corn/soy/cotton preferred)
• Strong modeling and data analysis skills in R and/or Python
• Experience with APSIM, DSSAT or comparable crop models
• Experience handling large agronomic datasets and geospatial data
• Ability to communicate model assumptions and uncertainty to diverse audiences

Responsibilities

• Develop G x E x M interaction models for various management scenarios across diverse geographies
• Calibrate, validate, and perform sensitivity analyses on models to quantify performance and uncertainty
• Work with large datasets from planters, sprayers, harvest data, soil maps, and imagery
• Design repeatable workflows for processing, summarizing, and visualizing model outputs
• Collaborate with agronomists, data scientists, software developers, and product managers to scale and communicate model results

Job description

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). We are seeking a technically skilled individual to support the development of Genotype x Environment x Management models that provide agronomic insights for internal stakeholders and farmers. This individual will leverage large agricultural datasets from connected equipment (planters, sprayers, combines, etc.), weather data, soil maps, satellite and drone imagery, and other sources to build scalable agronomic decision-support models to improve productivity, profitability, and sustainability in corn, soybean, and cotton production systems. They will be responsible for ensuring that agronomic models are agronomically reasonable, properly calibrated and validated, transparent in their assumptions, and appropriate for the crops, geographies, and management systems in which they are deployed. This position requires someone who has a deep understanding of agronomy in US corn, soy, or cotton production systems and experience building G x E x M models for predicting the outcomes of various management scenarios (planting date, variety selection, fertility management, crop care, etc.) across diverse geographies. This person will function as the agronomic modeling bridge between the technical development team and real-world corn, soybean, and cotton production systems.

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

• Experience 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.
• Experience 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 Client Operations Center and 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 tools.

Notes:

Person can be onsite, but will 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.

VIVA is an equal opportunity employer. All qualified applicants have an equal opportunity for placement, and all employees have an equal opportunity to develop on the job. This means that VIVA will not discriminate against any employee or qualified applicant on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status