AI​/ML Technical Expert – Predictive safety & multimodal biology

Company Description

Syngenta Crop Protection is a leader in agricultural innovation, bringing breakthrough technologies and solutions that enable farmers to grow healthy and nutritious food while taking care of the planet. We offer a leading portfolio of crop protection solutions for plant and soil health, as well as digital solutions that transform the decision-making capabilities of farmers. Our 17,900 employees serve to advance agriculture in more than 90 countries around the world.

Syngenta Crop Protection is headquartered in Basel, Switzerland, and is part of the Syngenta Group.

Syngenta Crop Protection is a leader in agricultural innovation, bringing breakthrough technologies and solutions that enable farmers to grow healthy and nutritious food while taking care of the planet. We offer a leading portfolio of crop protection solutions for plant and soil health, as well as digital solutions that transform the decision-making capabilities of farmers. Our 17,900 employees serve to advance agriculture in more than 90 countries around the world.

Syngenta Crop Protection is headquartered in Basel, Switzerland, and is part of the Syngenta Group. Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. Regardless of your position, you will have a vital role in safely feeding an ever-increasing population while taking care of our planet. Join us and help shape the future of agriculture.

We have an exciting opportunity for a a scientifically rigorous and innovative AI/ML Technical Expert to advance predictive safety and strengthen early research decision-making through data-driven and mechanistic approaches. Within the role working at the interface of biology, computational science, and artificial intelligence you will translate priority safety questions into robust modelling strategies, interpretable insights, and scalable analytical capabilities. The role will contribute to the development of a predictive safety framework that enables earlier identification of risk, improves mechanistic understanding, and enhances the quality and confidence of decision-making across research programs.

Key responsibilities will include:

• Partnering with toxicologists, biologists, chemists, bioinformaticians, and data scientists to define priority questions for predictive safety.
• Leading the development and evaluation of advanced AI/ML methodologies and predictive modelling approaches to address strategic safety questions.
• Applying machine learning, artificial intelligence, statistical modelling, and knowledge-driven approaches to extract meaningful biological signals (such as mechanisms of toxicity, AOPs, and safety-related outcomes) from large, complex, and heterogeneous datasets.
• Translating modelling outputs into actionable scientific insight that supports project and portfolio decisions.
• Evaluating Emerging AI, modelling, and computational-biology methods, while identifying applications that offer practical scientific value.
• Communicating complex computational findings clearly and credibly to multidisciplinary audiences.

• PhD (or equivalent experience) in Computational Biology, Bioinformatics, Systems Biology, Computational Toxicology, Data Science, Computer Science, Biomedical Engineering, or a related discipline.
• Demonstrated experience integrating diverse biological data types and extracting meaningful biological insight from complex, high-dimensional datasets.
• Strong programming skills in Python and experience with modern data science and machine learning frameworks.
• Proven experience in developing, validating and applying AI/ML, statistical, or computational biology approaches to address biological questions, while building reproducible computational workflows, analytical pipelines, and scientific software tools.
• Self-starter with enthusiasm, flexibility and desire to learn and apply new skills.

• Experience in computational toxicology, predictive safety, agrochemical research, pharmaceutical discovery, environmental science, or another applied research setting.
• Familiarity with adverse outcome pathways, mode-of-action frameworks, systems toxicology, network biology, or causal inference.
• Experience with chemical informatics, molecular descriptors, structural alerts, read‑across, QSAR, or exposure- and hazard-modelling approaches.
• Background in applying AI to biological or toxicological data, including multimodal learning, graph-based methods, foundation models, or representation learning with experience in using phenotypic imaging, cell painting, transcriptomic, metabolomic, or other molecular data as supporting evidence for model development or mechanistic interpretation.

Jealott's Hill International Research Centre UK is situated in pleasant semi-rural surroundings between Bracknell and Maidenhead and is the place of work for approximately 800 Syngenta…