Job Description

In this role, you will apply predictive modeling techniques to assess product efficacy, crop response, etc. in field trials and controlled environments. Your work will involve analyzing complex biological and environmental interactions to improve decision-making. You will collaborate with cross-functional teams to develop and refine models that enhance our understanding of crop yield, pest control, and environmental sustainability.

Beyond technical contributions, you will play a strategic role in identifying opportunities for model-driven insights, advising on experimental design for optimal data collection, and working closely with software developers to scale predictive tools for broader scientific use. Your expertise will help shape how we design trials, interpret results, and accelerate innovation in crop protection.

• Develop and apply data-driven and mechanistic models to predict biological efficacy and crop response, optimizing product performance through analysis of large-scale biological, environmental, and agronomic datasets.
• Collaborate with multidisciplinary teams to integrate predictive models into R&D workflows, including geospatial analysis using GIS software, and work with R&D IT to build data connections and deploy customer-facing applications.
• Provide guidance on experimental design to enhance data quality and modeling outcomes, while merging data from multiple sources for comprehensive analysis.
• Contribute to a global Center of Excellence model, collaborating with colleagues across functions to investigate interdisciplinary and multiscale modeling approaches that address fundamental business challenges.
• Stay abreast of new developments in the field, monitoring modeling approaches used by other companies, vendors, and academia, and explore new analytical tools and methodologies for potential deployment.
• Drive value for the business by identifying patterns and insights from data analysis and translating these findings into actionable recommendations for decision-making processes.

Qualifications

Required:

• Advanced degree (MSc or PhD) in a relevant field such as Computational Biology, Data Science, Environmental Science, or a related discipline with 3+ years’ experience. Work level will be adjusted based on education and experience. ****
• Excellent foundation in predictive modeling, data analytics, and computational methods.
• Proficiency in programming languages such as Python, R, MATLAB, or similar tools for data analysis and model development.
• Excellent knowledge of database query languages (e.g., SQL).
• Experience in biological, ecological, or environmental modeling, with applications in areas such as pest dynamics, disease epidemiology, ecotoxicology, or environmental fate modeling.
• Background in machine learning or AI-driven approaches applied to biological data.

Preferred:

• Familiarity with agricultural systems, crop protection, or agronomic practices.
• Experience with spatial modeling, GIS analysis, or remote sensing for agricultural applications.

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 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.

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