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Enviromics Scientist

Syngenta
paid holidays, 401(k), profit sharing
United States, North Carolina, Durham
9 Davis Drive (Show on map)
Jun 24, 2025

As an Enviromics Scientist, you will work collaboratively on developing and implementing industry-leading environmental modeling approaches to improve decision-making for Syngenta Seeds Research and Development. The ideal candidate should be creative and use their knowledge and experience in environmental sciences, satellite-based data analysis, and machine learning to revolutionize our seeds development pipeline and enable best-in-class insights and decision-making for field crops such as corn, soy, sunflower, and cereals.

Duties:

  • Develop scalable envirotyping pipelines to support digital germplasm development innovation for diverse crops globally.
  • Catalog and maintain envirotyping data to advance products and characterize testing environments.
  • Design and operationalize AI/ML satellite-based approaches for phenomics and enviromics, including GIS, spatial modeling, and both supervised and unsupervised data-driven solutions for GxExM elucidation.
  • Collaborate directly with biostatisticians, applied crop teams, and other scientists across various Syngenta business units, engaging stakeholders and experts across multiple R&D domains.

Company Description

Syngenta Seeds is one of the world's largest developers and producers of seed for farmers, commercial growers, retailers and small seed companies. Syngenta seeds improve the quality and yields of crops. High-quality seeds ensure better and more productive crops, which is why farmers invest in them. Advanced seeds help mitigate risks such as disease and drought and allow farmers to grow food using less land, less water and fewer inputs.

Syngenta Seeds brings farmers more vigorous, stronger, resistant plants, including innovative hybrid varieties and biotech crops that can thrive even in challenging growing conditions.

Syngenta Seeds is headquartered in the United States.

Qualifications

  • PhD or Master's degree in Computational Agronomy, Forestry, Ecology, Geosciences, Data science, or a related scientific field.
  • 2+ years of proven experience in quantitative methods to explore spatial-temporal variability in satellite-based image analysis; including programming and remote sensing skills, statistics and machine learning or large language models (LLM).
  • Demonstrated experience in environmental sciences (soil, weather or diseases risks), crop growth models or crop simulation models, with ability to handle techniques for data collection and data curation in this realm.
  • Technical understanding of quantitative methods for predictive analytics in seeds research.
  • Experience building and sharing documented, reusable software libraries for scientific efforts and a multidisciplinary community.
  • Excellent oral and written communication skills in English, including the ability to communicate effectively with individuals of diverse language, cultural, and scientific backgrounds.

Additional Information

What We Offer:

  • A culture that celebrates diversity & inclusion, promotes professional development, and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs.
  • Full Benefit Package (Medical, Dental & Vision) that starts your first day.
  • 401k plan with company match, Profit Sharing & Retirement Savings Contribution.
  • Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits.

Syngenta has been ranked as atop employerby Science Journal.

Learn more about ourteamand ourmission here: https://www.youtube.com/watch?v=OVCN_51GbNI

Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.

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