Data Scientist – Agriculture

Overview

As a Data Scientist in the Global Data Analytics & Predictive Science Team, you will transform historical biological data into actionable insights for active ingredient development across R&D. You’ll apply machine learning to interpret experimental outcomes and support the design and optimization of crop protection solutions. Your work will enhance understanding of biological performance and guide trial design, modelling, and data-driven decision making. You will collaborate with cross-functional teams and engage in digital transformation initiatives to accelerate impact.

Pay / Benefits

• generous pension scheme
• bonus scheme
• private medical & life insurance
• flexible working
• extensive training options
• international environment

Responsibilities

• Analyze historical biological field trial data to identify patterns and assess factors affecting product performance
• Support domain experts by uncovering analytics opportunities that drive business value
• Interpret diverse datasets (physical chemistry, biokinetic, formulation, marketing, environmental) to aid decision making and trial design
• Guide technical managers in designing field trials to validate hypotheses and model predictions
• Collaborate with R&D IT and software developers to improve data-model integrations and deploy tailor-made applications
• Monitor new modelling approaches and analytics tools for potential use
• Contribute to digital transformation projects to boost data science impact on predictive field trialing
• Engage with internal and external collaborators and integrate complementary capabilities into projects

Key requirements

• Postgraduate-level foundations in data science with applications to natural sciences
• Proven experience with core data science libraries for analytics, modelling, and visualization in Python
• Scientific domain knowledge in environmental sciences or biology
• Experience developing ML models for biological or crop protection outcomes
• Experience with generative AI approaches is a plus
• Ability to extract insights from data and communicate concepts to technical and non-technical audiences
• Adaptability to diverse data types and analytical tools, with strong problem-solving and collaboration across teams

• adaptability
• cross-functional collaboration
• clear data storytelling
• Python data-science libraries (ML and DL)
• data analysis and visualization
• machine learning model development

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