AI/ML Technical Expert – Predictive safety & multimodal biology

Syngenta Crop Protection; Stein; Vor Ort; CHF 120.000 - 180.000; Vollzeit

Benefits dieser Stelle

International environment

Open culture

Training options

Pension plan

Onsite doctor

Canteen

Family-friendly benefits

Zusammenfassung

Syngenta Crop Protection is seeking an AI/ML Technical Expert to advance predictive safety and multimodal biology. You will bridge biology, computation, and AI to turn priority safety questions into robust modelling strategies and interpretable insights, enabling earlier risk identification and stronger decision making across research programs.

Join a world-leading team in Switzerland focused on innovative crop protection.

Qualifikationen

• PhD (or equivalent) 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.
• 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.

Aufgaben

• Partner 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 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.

Kenntnisse

Python

AI/ML

Data science

Multimodal learning

Reproducible workflows

Ausbildung

PhD in Computational Biology

Related discipline

Tools

TensorFlow

PyTorch

scikit-learn

Jobbeschreibung

AI/ML Technical Expert - Predictive safety & multimodal biology

• Full-time

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.

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 Evaluate 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.

What we are looking for

• 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.

What we offer

We offer a variety of financial and non‑financial benefits including:

• A position which contributes to valuable and impactful work in a stimulating and international environment
• A superb working environment with an open culture and diverse workforce where new ideas are always welcome
• The opportunity to work with and learn from highly qualified and experienced employees and gain scientific and technical excellence
• Learning culture (Together we Grow) and wide range of training options
• You will profit from a competitive pension fund plan, flexible working, attractive bonus system, onsite doctor, fitness room, canteen, and other benefits such as Family friendly initiatives, Child and Family allowance

Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.