AI/ML Technical Expert – Predictive safety & multimodal biology

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.

At Syngenta Crop Protection, we're pioneering solutions that safeguard global food security while championing sustainable agriculture. As a world market leader headquartered in Switzerland, we empower farmers with innovative crop protection technologies that defend against nature's toughest challenges. We unite advanced science with digital solutions to develop intelligent crop protection that maximizes yields while minimizing environmental impact. Join our mission of revolutionizing plant protection from seed to harvest. Syngenta has been ranked as a top employer by Science Magazine, and it has been awarded with the "Friendly Work Space" label to all its Swiss sites.

• 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.