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

• 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

We offer a variety of financial and non-financial benefits including:

• • A position which contributes to valuable and impactful work in a stimulating and international environment
  • A superb working environment with an open culture and diverse workforce where new ideas are always welcome
  • The opportunity to work with and learn from highly qualified and experienced employees and gain scientific and technical excellence
  • Learning culture (Together we Grow) and wide range of training options
  • You will profit from a competitive pension fund plan, flexible working, attractive bonus system, onsite doctor, fitness room, canteen, and other benefits such as Family friendly initiatives, Child and Family allowance

Please send your complete application with CV, a motivation letter that highlights your motivation and relevant experience for this position, diploma and work certificates (if applicable) in English. Additionally, we welcome access to git repositories showcasing relevant past projects. We will only consider applications that contain CV and motivation letters.

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.