Engineer Product II - Agronomic Functional Systems

Epitec; Johnston (IA); Remote; USD 69,000 - 73,000; Full time

Job summary

Epitec seeks a Crop Modeling Scientist to develop, calibrate, and apply advanced agricultural simulation models that support data-driven decisions across diverse crop production systems. You will leverage agronomic expertise, crop modeling, data science, and AI-enabled analytics to evaluate GxE interactions and management effects.

The role involves collaborating with agronomists, data scientists, software engineers, and product teams to translate model outputs into practical grower

Qualifications

• Experience handling large multi-year, multi-environment agricultural data sets.
• Ability to translate scientific research into actionable, operational recommendations.
• Experience designing and evaluating AI-enabled analytics in agriculture.

Responsibilities

• Develop and calibrate crop models and evaluate GxExM interactions.
• Design calibration, validation, sensitivity and uncertainty analyses.
• Define modeling requirements and validation criteria.
• Analyze large geospatial and environmental datasets.
• Build scalable workflows for data processing and reporting.
• Collaborate with engineers and product teams to deliver decision-support tools.

Skills

Python

R

SQL

Databricks

Cloud platforms

APIs

AI-enabled analytics

Geospatial analytics

Education

Bachelor's/Master's/PhD in Agronomy or related field

Tools

Tableau

Power BI

GIS software

Job description

Title: Crop Modeling Scientist

Location: Open to fully remote candidates

Details: Contract with ongoing need, opportunity for direct hire, fully onsite role

Pay Rate: $50.00-53.00/hr. with benefit inclusions

First shift - 8am-5pm CST business hours

Job Summary

We are seeking a Crop Modeling Scientist to develop, calibrate, and apply advanced agricultural simulation models that support data-driven decision-making across diverse crop production systems. This role will leverage agronomic expertise, crop modeling, data science, and AI-enabled analytics to evaluate interactions among genetics, environment, and management practices. The ideal candidate will collaborate across multidisciplinary teams to transform complex agricultural data into scientifically sound recommendations and scalable decision-support solutions for growers.

Responsibilities

• Develop and apply crop modeling approaches to evaluate genotype × environment × management (GxExM) interactions and predict outcomes of management decisions across diverse crop production systems.
• Design and execute model calibration, validation, sensitivity, and uncertainty analyses across geographies, years, and management systems.
• Define modeling requirements by evaluating interactions among genetics, weather, soils, crop management, and cropping history, including model assumptions, inputs, outputs, and validation criteria.
• Analyze large agricultural, geospatial, and environmental datasets, including machine-generated field data, soil maps, topography, remote sensing products, multispectral imagery, and other environmental layers.
• Develop scalable, repeatable workflows for data processing, simulation, visualization, and reporting.
• Establish agronomic logic, constraints, and validation frameworks that ensure AI-generated recommendations are scientifically sound, transparent, and operationally effective.
• Collaborate with agronomists, data scientists, software engineers, and product teams to translate model outputs into practical decision-support tools and grower recommendations.

Years of Experience and Education

• Bachelor's, Master's, or Ph.D. in Agronomy, Crop Science, Soil Science, Agricultural Engineering, Biological Systems Engineering, Quantitative Genetics, or a related field.
• Experience working with large multi-year, multi-environment agricultural datasets and translating scientific research into operational recommendations and decision-support tools.

Skills Required

• Expertise in crop physiology, phenology, soil-water dynamics, nutrient cycling, and U.S. row crop production systems, preferably corn and soybean.
• Advanced proficiency in Python and/or R, with experience using SQL, Databricks, cloud platforms, APIs, and AI-assisted development tools.
• Experience developing reproducible analytics workflows and working with large agricultural, geospatial, remote sensing, drone, satellite, and environmental datasets.
• Knowledge of precision agriculture technologies, machine-generated farm data, field boundaries, management zones, digital elevation models, and related agronomic data layers.
• Strong analytical, statistical, and problem-solving skills, with the ability to communicate model assumptions, uncertainty, limitations, and results to both technical and non-technical audiences.
• Experience developing agronomic validation frameworks for AI-generated recommendations and utilizing visualization tools such as Tableau, Power BI, or similar platforms.

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