UK remote
Senior Data Scientist - AI and GenAI
About this role
Join our global analytics team within Product Biology & Sustainable Innovation and help shape the use of predictive science in Crop Protection R&D. As a Senior Data Scientist – Predictive Science, you will develop machine-learning and predictive-modelling approaches that guide the discovery, optimisation and development of Crop Protection solutions. You will connect diverse scientific data with business and R&D decision-making, working closely with multidisciplinary teams to generate robust insights and accelerate complex decisions.
This role offers the opportunity to work autonomously on high-impact scientific challenges, influence modelling approaches and data strategy, and collaborate with experts across an international research environment. What you will do Develop and validate predictive models based on historical, experimental and scientific data to support project and business decisions. Apply AI, machine learning and Generative AI methods to accelerate scientific discovery, improve decision-making and create scalable solutions across Crop Protection R&D.
Identify, assess and prioritise AI and GenAI use cases based on scientific relevance, business value, feasibility, data readiness, risk and expected impact. Partner with multidisciplinary teams to support the discovery, optimisation and development of Crop Protection leads and product candidates. Integrate and analyse data from different sources, including physical chemistry, biokinetic, environmental, laboratory, glasshouse and field-trial data.
Work with scientists to understand complex problems and identify opportunities where predictive modelling, AI or GenAI can create measurable value. Collaborate with R&D IT and software development teams to build data connections and deploy analytical, AI-enabled or customer-facing applications. Ensure the responsible, transparent and secure use of AI and GenAI, including appropriate model validation, documentation, reproducibility, data governance and risk management.
Establish and track KPIs, model performance measures and success metrics for predictive modelling and AI initiatives to demonstrate scientific and business impact. Communicate complex analytical and AI-driven insights to non-technical stakeholders through clear visualisations, practical recommendations and executive summaries. Lead and mentor junior data scientists and analysts, fostering a culture of continuous learning and knowledge sharing around AI and machine-learning best practices.