Senior Data Scientist - AI and GenAI

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.
Stay abreast of emerging AI, machine-learning and GenAI technologies, methodologies and industry developments to ensure the organisation remains at the forefront of predictive science innovation.
Develop and maintain comprehensive documentation of models, methodologies and analytical frameworks to ensure reproducibility, auditability and knowledge transfer across the team.
Optimise model performance and scalability, ensuring analytical solutions can handle increasing data volumes and computational demands across the R&D portfolio.
Manage stakeholder relationships and provide strategic guidance on the prioritisation and sequencing of AI and predictive-modelling initiatives aligned with business objectives.
Essential
Advanced academic background in Data Science, Machine Learning, Statistics, Mathematics, Physics or a related quantitative discipline; postgraduate education is strongly preferred.
Demonstrated experience developing machine-learning, AI or Generative AI-enabled predictive models for biological, scientific, agricultural, life-science or Crop Protection-related outcomes.
Strong knowledge of Python and relevant data science, analytics, modelling, AI and Generative AI tools and frameworks.
Practical experience with Generative AI, including Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and the evaluation of GenAI outputs.
Strong understanding of model validation, robustness assessment and the interpretation of predictive-model performance, including the reliability, limitations and risks associated with AI and GenAI solutions.
Ability to assess and select the most appropriate solution approach-such as predictive modelling, conventional machine learning, automation or Generative AI-based on the problem, data availability, feasibility, expected value and risk.
Knowledge of responsible AI principles, including data privacy, security, explainability, bias awareness, governance and human oversight.
Experience analysing complex datasets and translating findings into clear, actionable insights.
Experience collaborating with scientists, data scientists, software engineers or technical specialists to solve complex problems and deploy AI-enabled solutions.
Strong analytical thinking, creative problem-solving and intellectual curiosity.
Ability to work independently, manage priorities effectively and deliver work to a high standard of accuracy.
Strong communication skills, including data visualisation and storytelling for specialist and non-specialist audiences.
Professional proficiency in English.
Preferred
Academic or professional experience in biology, chemistry, ecology, environmental science, biokinetics or related scientific domains.
Experience working with laboratory, field-trial, environmental or other complex scientific datasets.
Experience with interdisciplinary or multiscale modelling approaches.
Experience building, evaluating or deploying AI/GenAI applications in scientific, R&D or regulated environments.
Experience collaborating with external partners, vendors, academic institutions, or R&D IT teams on data platforms, AI solutions and analytical applications.
Key Capabilities
Adaptability across different scientific questions, data types, analytical methods, AI techniques and Generative AI applications.
Ability to build trusted relationships and collaborate effectively across functions.
Ownership, customer focus and the ability to influence through technical expertise.
Ability to translate scientific and business needs into practical, responsible and measurable AI-enabled solutions.

Employment type
Full-time

Work arrangement
No