Data Scientist

at  Syngenta Crop Protection

Company Description
As a world market leader in crop protection, we help farmers to counter the threats to our crops 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

• Works in multidisciplinary project teams to bring data analytics and modelling skills to the discovery, optimization and development of crop protection leads or product candidates
• Interacts with domain experts to understand scientific problems and identify opportunities where the adoption of models will drive value for the business
• Works with colleagues in adjacent fields of predictive science to investigate interdisciplinary and multiscale modelling approaches to solve fundamental challenges in active ingredient development for crop protection
• Drives strategic business initiatives across Crop Protection R&D e.g. working with physical chemistry, biokinetic and environmental data to model laboratory, glasshouse and field trials.
• Works with R&D IT and software developers to build data connections and deploy customer facing applications
• Merges data from multiple data sources together to prepare them for analysis
• Monitors new developments in the field and maintains awareness of modelling approaches taken by other companies, vendors, and academia. Explores new analytical tools, methodologies or approaches to understand their effectiveness and how to deploy them
• Works with external collaborators to understand and integrate their complementary capabilities into projects and programs
• Acquire knowledge from Syngenta internal expert modellers to build personal network and quickly get up to speed with existing modelling opportunities, and to acquire domain knowledge
• Support active ingredient projects by using analytical and modelling methods
• Engage with high-priority digital transformation projects to understand opportunities to accelerate the impact of data science for predictive field trialing