Job Description
At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta’s Digital & Data Science Group in Biologicals Research is seeking a Data Scientist Biologicals Research in St. Johann – Basel (Switzerland) or Jealott’s Hill (UK). This role will support the delivery of 'omics, phenotypic, and environmental data to identify patterns in product performance and make predictions. You will analyze and interpret scientific experiments using analytical skills and machine learning approaches, guiding the selection, optimization, and development of novel biological solutions.
Biologicals research discovers, characterizes, optimizes, and produces novel biologicals derived from nature—naturally occurring microbes, extracts, or molecules that protect plants from pests and diseases or improve plant and soil health. In this role, you will work with Syngenta.
- Learn and understand the Biologicals data landscape, key data attributes, and experimental capabilities to identify opportunities for enabling and enhancing science through the deployment of analytical methods and multi-omics approaches.
- 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.
- Curate large-scale omics datasets for biomarker, mode of action, and trait discovery, while identifying data needs and providing recommendations to scientists to ensure data quantity and integrity for analyses.
- Shape Biologicals data analysis strategy and work collaboratively to deliver and deploy new approaches, share learning, and drive innovation in digital and data science including technology foresight.
- Work with R&D IT and software developers to improve predictive model deployment applications tailored to stakeholder needs, and support business users with change management initiatives to manage data more effectively.
Qualifications
Required Qualifications:
- MSc or PhD in Data Science, Statistics, Machine Learning, Computational Biology, Bioinformatics, or related field with experience in natural sciences (e.g., chemical biology, ecology, environmental sciences).
- 3+ years of experience applying machine learning to complex biological, biochemical, environmental, or agricultural datasets.
- Strong understanding of experimentally derived biological data, data types, and appropriate analysis methods, including multivariate statistics.
- Strong proficiency in Python and/or R, UNIX/Linux environments, and ML frameworks.
- Proven ability to handle large datasets and use data analysis packages for 'omics data analysis and data visualization.
Desired Qualifications:
- Demonstrated ability to inspire and collaborate with others to achieve excellence in scientific work.
- Strong passion for innovation and problem-solving with the ability to work effectively in a dynamic, fast-paced environment.
- Experience working cross-functionally with IT, software developers, and business stakeholders to deploy data science solutions.
Additional Information
Jealott's Hill International Research Centre UK is situated in pleasant semi-rural surroundings between Bracknell and Maidenhead and is the place of work for approximately 800 Syngenta scientists and support staff. Jealott’s Hill is one of the main global research and development sites and key activities include research into discovery of new active ingredients, new formulation technologies, product safety and technical support of our product range.
What We Offer:
A culture that celebrates diversity & inclusion, promotes professional development, and strives for a work-life balance that supports the team members.
Extensive benefits package including a generous pension scheme, bonus scheme, private medical & life insurance.
Up to 31.5 days annual holiday plus 8 UK bank holidays.
36-hour working week.
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.
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