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Syngenta Crop Protection AG Forschung & Entwicklung is seeking a Data Scientist (m/f/d) to advance multi-omics data integration and machine learning within the Digital & Data Science Group of Biologicals Research. You will work on omics-driven product insights and develop tools to guide identification, optimization, and development of novel Biologicals.
You will collaborate with scientists and IT to deploy predictive models and communicate findings to both technical and non-technical audiences,
Syngenta Crop Protection AG Forschung & Entwicklung
Vacant since : 18.08.2026 Number of jobs : 1 4332 Stein AG (AG) 100% Immediately Permanent
Company Description
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.
Job Description
We are seeking a highly skilled Data Scientist (m/f/d) with deep expertise in developing multi-omics data integration and machine learnings methods, to join our Digital & Data Science Group in Biologicals Research. Biologicals research discovers, characterizes, optimizes and produces novel biologicals. Biologicals are derived from nature. They can be naturally occurring microbes, extracts or molecules protecting plants from pests and diseases or improving plant and soil health.Within this role you will work on Syngenta ‘omics data to identify patterns in product performance and make predictions. You will be asked to develop methods to analyze and interpret the outcome of ‘omics experiments with your analytical skills as well as machine learning approaches. Your work will result in new tools to guide selection, optimization and development of novel Biologicals solutions.
Develop and implement new multi-omics integration strategies (e.g., data harmonization, feature engineering, network-based methods) to strengthen biological interpretation and hypothesis generationApply and advance machine learning, statistical modeling, and AI approaches to extract insights from complex, high‑dimensional, multi‑omics datasetsCurate, quality control, and integrate large scale ’omics datasets, ensuring data integrity, reproducibility, and downstream analytical readinessEvaluate and develop new data analysis tools, validate findings using a trial and iterative approach, and effectively communicate findings to technical and non-technical audiencesIdentify data needs and provide recommendations to scientists to ensure the quantity and verify the integrity of data used for analysesWork collaboratively to deliver new approaches, share learnings, and drive innovation in digital and data science including technology foresightWork with R&D IT and software developers to deploy predictive model applications tailored to stakeholder needsSupport business users with change management initiatives to manage data more effectively
MSc or PhD in Data Science, Statistics, Machine Learning, Computational Biology, Bioinformatics, or related field with some experience in natural sciences (e.g. chemical biology, microbiology, ecology, environmental sciences)3+ years developing multi-omics integration methods for complex biological, biochemical, environmental, or agricultural datasetsStrong proficiency in Python and/or R, UNIX/Linux environments, ML frameworks, SQLExperience with proteomics and metabolomics data analysis would be an assetDynamic personality with passion for innovation and problem-solving
We offer a variety of financial and non-financial benefits including:
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.