Data Scientist (m​/f​/d

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.

Key responsibilities will include:

• 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