Data Scientist Biologicals Research at SyngentaGroup

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‑dimensional, multi‑omics datasets  • Design and implement multi-omics integration strategies (e.g., data harmonization, feature engineering, network-based methods) to strengthen biological interpretation and hypothesis generation  • Curate, quality control, and integrate large scale plant ’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