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Data scientist biologicals research

Basel
Syngenta
Data Scientist
Inserat online seit: Veröffentlicht vor 10 Std.
Beschreibung

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.
Job Description
We are seeking a highly skilled
Data Scientist
with deep
expertise in machine learning and multi-omics data integration
, coupled with hands-on experience working with plant and/or microbial ’omics datasets (e.g., genomics, transcriptomics, proteomics, metabolomics) 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, phenotypic and environmental data to identify patterns in product performance and make predictions. You will be asked to analyze and interpret the outcome of scientific experiments with your analytical skills as well as machine learning approaches. Your work will guide selection, optimization and development of novel biological solutions.
Key Responsibilities Will Include

Learn and understand the Biologicals data landscape, key data attributes and experimental capabilities to identify opportunities for enabling and enhancing our science through the deployment of analytical methods and multi-omics approaches
Apply and advance machine learning, statistical modeling, and AI approaches to extract insights from complex, high‑dim

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