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# Agronomic Systems Engineer

- **Company:** Aditi Consulting
- **Location:** United States
- **Salary:** $104,000.0 - $116,480.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Decision Support Systems, R (Programming Language), Python (Programming Language), Power BI, SQL Databases, Tableau (Software), Data Layers, Databricks
- **Published:** October 2, 2026
- **Apply:** https://www.dice.com/job-detail/491e220d-cbf5-42d8-9d00-0f78327d3b4c

## About the Role

* Experience with APSIM, DSSAT, or comparable process-based crop models for agricultural decision support.
 * Strong understanding of crop physiology, phenology, soil water dynamics, and nutrient cycling.
 * Expertise in corn and soybean production preferred.
 * Advanced proficiency in R and/or Python for data analysis and model development.
 * Experience with Databricks, SQL, cloud computing environments, and APIs.
 * Demonstrated experience with AI-assisted development.
 * Experience with large multi-environment, multi-year, or multi-management agricultural datasets.
 * Ability to communicate model assumptions, results, and limitations to diverse audiences.
 * Master's or Ph.D. in agronomy, crop science, soil science, or a related discipline., * Experience with precision agriculture technologies and Operations Center.
 * Familiarity with machine-generated datasets and common agronomic data layers.
 * Experience in agronomic model development within the agricultural industry.
 * 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.

## Description

This role involves developing and implementing advanced modeling approaches for predicting outcomes of various management scenarios in corn, soybean, and cotton production systems. The position requires collaboration with agronomists, data scientists, software developers, and product managers to ensure the models are agronomically sound and scientifically defensible., * Develop modeling approaches for G x E x M interactions to predict outcomes of management scenarios in diverse geographies.

 * Design and execute model calibration, validation, and sensitivity analyses to quantify model performance and limitations.
 * Understand interactions among genotype, weather, soil, management, and cropping history to define modeling problems and criteria.
 * Work with large, machine-generated agricultural datasets and geospatial datasets.
 * Develop repeatable workflows for processing, summarizing, and visualizing model outcomes.
 * Develop agronomic logic, constraints, and validation frameworks for AI-generated recommendations.
 * Collaborate with various teams to build, scale, and communicate model outcomes.