Agricultural Data Scientist at Syngenta Group

Denver, Colorado, United States -

Full Time

Start Date

Immediate

Expiry Date

20 May, 26

Salary

110000.0

Posted On

19 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Crop Modeling, UAS Thermal Imagery, Data Analysis Workflows, Water Use Efficiency, Model Development, Data Processing Pipelines, Experimental Design, Statistical Analysis, Python, Geospatial Datasets, Cloud Environments, Machine Learning, Two-Source Energy Balance Model, Evapotranspiration, Applied Statistics, Generative AI

Industry

Farming

Description

Company Description As a world market leader in crop protection, we help farmers to counter these threats and ensure enough safe, nutritious, affordable food for all –while minimizing the use of land and other agricultural inputs. Syngenta Crop Protection keeps plants safe from planting to harvesting. From the moment a seed is planted through to harvest, crops need to be protected from weeds, insects and diseases as well as droughts and floods, heat and cold. Syngenta Crop Protection is headquartered in Switzerland. Job Description 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's Soil Science team is seeking an Agricultural Data Scientist in Denver, CO. This role will lead the development and implementation of advanced crop modeling systems that leverage UAS thermal imagery and ground-based measurements to optimize water use efficiency in agricultural systems. Design and execute sophisticated data analysis workflows that transform complex agricultural datasets into actionable insights for product development and agronomic recommendations. The Agricultural Data Scientist will drive innovation in computational approaches to understand plant-soil relationships through the creation of scalable models and automated data processing pipelines; serving as a technical expert in experimental design and statistical analysis, while effectively communicating findings to diverse stakeholders through compelling visualizations and presentations that influence decision-making across the organization. Accountabilities: Lead the development and implementation of advanced two-source energy balance models utilizing UAS thermal imagery and ground-truth measurements to deliver accurate crop water stress assessments across diverse agricultural environments. Independently design, build, and validate sophisticated scalable models that enhance crop water use efficiency research, ensuring alignment with business objectives and regulatory requirements. Systematically analyze and integrate complex agricultural datasets to establish robust plant-soil relationships, driving improvements in crop performance and sustainability metrics. Execute comprehensive analysis of multi-location field experiments, including rigorous statistical validation and quality assessment, to generate actionable insights supporting product development decisions and evidence-based agronomic recommendations. Architect and maintain efficient data processing pipelines using Python and other programming languages to systematically clean, transform, and analyze large-scale agricultural datasets, ensuring high-quality model development and validation. Qualifications Required: PhD degree in Agronomy, Soil Science, Civil and Environmental Engineering, or related field required. One year prior experience in agricultural research, crop consulting, digital agronomy, or related agricultural data analysis roles required. Experience working with geospatial datasets required. Experience in cloud environments such as Amazon AWS preferred. Experience with machine learning frameworks such as CatBoost, XGBoost, LightGBM, PyMC, and StatsModels. Strong proficiency in Python programming with experience in data analysis libraries (e.g. Pandas, NumPy, Scikit-learn) and visualization tools (e.g. Matplotlib, Seaborn, Plotly). Knowledge of crop production systems, including agronomic management practices, growth stages, yield-limiting factors, and crop protection practices preferred. Desired: Mastery of Two-Source Energy Balance model for evaluation and validation of crop evapotranspiration required. Familiarity and experience with UAS technology and data outputs. Understanding of applied statistics and experience applying AI/ML techniques, including supervised learning, time series forecasting, clustering, segmentation, and Bayesian inference desired. Demonstrated ability to leverage generative AI tools (ChatGPT, Claude, GitHub Copilot) to enhance productivity and accelerate problem-solving. Demonstration of the use of data stewardship skills and version-controlled work such as GitHub, GitLab. Experience collaborating with data engineers and ML engineers to develop, optimize, and deploy model solutions. Additional Information Salary for the role is $88,000 - $110,000 per year. 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 a top employer by Science Journal. Learn more about our team and our mission 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. WL: 4B #LI-DNI

Responsibilities

This role involves leading the development and implementation of advanced crop modeling systems using UAS thermal imagery and ground measurements to optimize water use efficiency. The scientist will design and execute complex data analysis workflows to translate agricultural datasets into actionable insights for product development and agronomic recommendations.