Data Scientist

Develop and implement new multi-omics integration strategies (e.g., data harmonization, feature engineering, network-based methods) to strengthen biological interpretation and hypothesis generation. Apply and advance machine learning, statistical modeling, and AI approaches to extract insights from complex, high‑dimensional, multi‑omics datasets. Curate, quality control, and integrate large scale ’omics datasets, ensuring data integrity, reproducibility, and downstream analytical readiness. Evaluate and develop new data analysis tools, validate findings using a trial and iterative approach, and effectively communicate findings to technical and non-technical audiences. Identify data needs and provide recommendations to scientists to ensure the quantity and verify the integrity of data used for analyses. Work collaboratively to deliver new approaches, share learnings, and drive innovation in digital and data science including technology foresight. Work with R&D; IT and software developers to deploy predictive model applications tailored to stakeholder needs. Support business users with change management initiatives to manage data more effectively.

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.

• 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 datasets

• Strong proficiency in Python and/or R, UNIX/Linux environments, ML frameworks, SQL

• Experience with proteomics and metabolomics data analysis would be an asset

• Dynamic personality with passion for innovation and problem-solving