Computation Agronomy Scientist

We are seeking a Computational Agronomy Scientist to join our Global Computational Agronomy Team. This role combines agronomic expertise with data science skills to develop intelligent recommendation systems and decision support models that optimize the use of Syngenta Seed and Crop Protection products. Working with interdisciplinary teams including agronomic scientists, data scientists, engineers, and digital product managers, you will create digital solutions that deliver value to customers worldwide through practical, data-driven agricultural innovations.

• Develop and implement agronomic models and data-driven recommendation systems for crop management practices, including product selection and timing, planting decisions, fertilization, irrigation, and harvest timing optimization.
• Design, build, and validate predictive models to support the Crop Protection portfolio, including fungicides, insecticides, herbicides, seed treatments, and biologicals.
• Analyze and integrate diverse agricultural datasets (field trial data, weather information, soil characteristics, pest monitoring data, agronomic scouting records) to generate actionable insights and support product development.
• Create data processing pipelines using Python to clean, transform, and analyze agricultural datasets, leveraging generative AI to automate tasks and optimize workflows.
• Communicate model outcomes and analytical findings effectively to technical and non-technical stakeholders through presentations, visualizations, and technical documentation.

Required Qualifications: 

Education: MS degree in Agronomy or related agricultural sciences with demonstrated experience in agricultural systems, applied data science, and technology (PhD preferred).

Experience: Minimum 2 years of professional/academic experience in agricultural research, crop consulting, digital agronomy, or related roles involving data analysis, model building and validation, and agronomic decision-making.

Technical skills:

• Proficiency in Python and/or R programming, with experience in data analysis, forecast models, and visualization tools.
• Demonstrated knowledge of computational and applied statistical methods, including AI/ML techniques such as supervised learning, time series forecasting, clustering, segmentation, and Bayesian inference.

Agronomic knowledge:

• Deep knowledge of crop production systems (soybeans, corn, wheat, cotton, canola), including agronomic management practices, growth stages, yield-limiting factors, and crop protection practices.
• Strong understanding of pest management, including pest and pathogen lifecycles, economic thresholds, ROI concepts, IPM principles, and disease modeling for outbreak forecasting.

Desired Qualifications:

• Experience with epidemiology or disease ecology.
• Familiarity with crop simulation models (DSSAT, APSIM) and geospatial datasets.
• Experience working in cloud environments (Amazon SageMaker) and collaborating with data engineers and ML engineers to deploy model solutions.
• Knowledge of Agile software development principles and related tools.
• Demonstrated ability to leverage generative AI tools (ChatGPT, Claude, GitHub Copilot) to enhance productivity and accelerate problem-solving.
• Practical knowledge of field-level agronomic research protocols, IoT devices, sensors, and field monitoring techniques.

• Candidates must be legally authorized to work in the country.
• Remote working is possible within the country.

What We Offer:

• An environment where every voice matters, professional growth is encouraged, and work-life balance is prioritized.
• Comprehensive benefits package that starts from day one, including health and wellness coverage tailored to your location.
• Generous annual leave entitlement, learning and development opportunities, wellness programs, employee assistance programs, and additional benefits in accordance with local practices.

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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