Data Scientist - Environment and Disease Modeling

Company Description About Syngenta At Syngenta Seeds Field Crops, we're shaping the future of agriculture and empowering farmers to meet the ever-growing demand for food and fuel. We’re a global Ag Tech powerhouse, headquartered in the United States, with passionate, local experts collaborating with farmers to deliver solutions that create market opportunities. We unite precision breeding, advanced biotechnology trait choice, and digital platforms for unmatched in-field performance. Our seeds help mitigate risks such as disease, insect, weed, and extreme weather pressures, all while promoting sustainable farming practices that protect and enhance our planet. Join our mission of revolutionizing food security and transforming agriculture.

At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta is seeking a Data Scientist - Environment and Disease Modeling in Field, IL. This role will support the plant epidemiology and disease science within the Temperate Hub R&D organization. This role combines expertise in environmental data analysis, plant disease modeling, and geospatial analytics to advance management across the breeding pipeline. In this role you will build and apply predictive models for environment and disease incidence and severity, link genotypic variation to disease susceptibility, and translate complex biological and environmental signals into actionable insights for breeders and agronomists. The Data Scientist - Environment and Disease Modeling will partner closely with germplasm development teams, pathologists, and global R&D colleagues to deliver disease resistance insights that directly inform selection decisions—both within a season and across multiple years and environments. Accountabilities: • Develop and maintain disease progression models integrating weather, soil, and agronomic data to predict incidence and severity across crops and geographies within season and across years • Integrate environmental and geospatial datasets (climate layers, remote sensing, field location metadata) to characterize biotic and abiotic stress landscapes and support site-based decision making and location management • Compare controlled environment and field data for inoculated trials, partner with pathologists and breeders to curate high-quality datasets for model training and validation, and translate phenotypic disease observations into quantitative traits • Design reproducible, scalable pipelines for data ingestion, QC, modeling, and visualization that can be deployed across the temperate hub breeding programs • Communicate findings and model outputs to scientific and non-scientific stakeholders through clear reports, dashboards, and/or presentations to support data-driven breeding strategy and management decisions • Collaborate with global teams to align environmental and disease modeling methods with broader platform architecture and data standards.