Data Scientist II GenAI

Short Summary

Data Scientist II GenAI with Syngenta Group. Join our global analytics team within Product Biology & Sustainable Innovation, where we use data, modelling and emerging AI technologies to accelerate scientific decision-making in Crop Protection. As a Mid Data Scientist – GenAI, you will develop and apply machine-learning and statistical modelling approaches to build a deeper understanding of biological performance. You will…

Key Details

• Position / opportunity: Data Scientist II GenAI
• Organization: Syngenta Group
• Country / coverage: United Kingdom
• Location: London, , United Kingdom
• Work arrangement: On-site
• Opportunity type: Jobs
• Sector: Agriculture
• Compensation: Not specified
• Duration: Not specified
• Start date: 2026-08-24
• Application deadline: Not specified

Description

Join our global analytics team within Product Biology & Sustainable Innovation, where we use data, modelling and emerging AI technologies to accelerate scientific decision-making in Crop Protection. As a Mid Data Scientist – GenAI, you will develop and apply machine-learning and statistical modelling approaches to build a deeper understanding of biological performance. You will work alongside scientists across biology, chemistry and environmental disciplines, translating complex scientific questions into practical analytical solutions. You will also help explore and apply generative AI, large language models, agentic workflows and knowledge graphs to scientific use cases. This is an opportunity to contribute to meaningful R&D initiatives whilst building expertise in responsible AI and predictive science within a highly collaborative, international environment. What you will do Develop predictive models using historical and experimental data to support decisions across Crop Protection R&D. Prepare, integrate and analyse heterogeneous datasets from internal and external sources, ensuring data quality and integrity throughout the analytical pipeline. Partner with scientific domain experts to identify high-value data science opportunities and translate them into practical analytical solutions. Support the design of experiments and field trials to enable effective modelling and analysis. Prototype and iterate on GenAI applications, including retrieval-augmented generation (RAG), literature knowledge extraction, summarisation and AI-assisted research workflows. Build and work with knowledge graphs to structure scientific relationships and provide grounded context for AI-driven reasoning. Develop agentic workflows that combine LLMs with tools, databases and APIs to automate multi-step scientific tasks. Support the evaluation, validation and governance of AI-powered tools, ensuring outputs are reliable, scientifically sound and appropriate for human decision-making. Work with R&D IT and software engineering teams to build data connections and deploy analytical tools or applications. Contribute to the development and documentation of data governance frameworks and best practices for responsible AI implementation within scientific workflows. Participate in knowledge-sharing initiatives, including documentation of methodologies, lessons learned and recommendations for future analytical projects. Keep up to date with developments in data science, machine learning and GenAI, assessing their practical value for scientific workflows and sharing insights with the broader team. In your first months, you will build Crop Protection domain knowledge, contribute to active projects using established analytical methods, and deliver one or two initial use cases that demonstrate measurable value. **Essential** Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, or a quantitative natural science; equivalent practical experience will also be considered. 1–4 years of relevant professional or research experience; internships, academic projects and thesis work may be considered. Strong foundation in machine learning, statistical modelling, experimental design and model validation. Proficiency in Python for data science and machine learning, including libraries such as 'pandas', 'scikit-learn', 'PyTorch' or 'TensorFlow'. Experience cleaning, integrating and preparing complex or heterogeneous datasets for analysis. Familiarity with software engineering best practices, including Git, testing, documentation and reproducible workflows. Strong analytical and critical-thinking skills, with the ability to evaluate AI outputs rather than simply apply solutions. Ability to collaborate with scientists or domain experts and communicate insights clearly to both technical and non-technical audiences. Professional proficiency in English. **Preferred** Familiarity with LLMs, embeddings, prompt engineering, retrieval-augmented generation and agentic AI frameworks such as 'LangChain', 'LangGraph', 'LlamaIndex' or 'CrewAI'. Experience with knowledge graphs, 'Neo4j', 'Cypher', 'SPARQL' or graph-based data structures. Familiarity with…

Responsibilities

• Join our global analytics team within Product Biology & Sustainable Innovation, where we use data, modelling and emerging AI technologies to accelerate scientific decision-making in Crop Protection.
• As a Mid Data Scientist – GenAI, you will develop and apply machine-learning and statistical modelling approaches to build a deeper understanding of biological performance.
• You will work alongside scientists across biology, chemistry and environmental disciplines, translating complex scientific questions into practical analytical solutions.
• You will also help explore and apply generative AI, large language models, agentic workflows and knowledge graphs to scientific use cases.
• This is an opportunity to contribute to meaningful R&D initiatives whilst building expertise in responsible AI and predictive science within a highly collaborative, international environment.
• What you will do Develop predictive models using historical and experimental data to support decisions across Crop Protection R&D.

Requirements / Eligibility

• Keep up to date with developments in data science, machine learning and GenAI, assessing their practical value for scientific workflows and sharing insights with the broader team.
• In your first months, you will build Crop Protection domain knowledge, contribute to active projects using established analytical methods, and deliver one or two initial use cases that demonstrate measurable value.
• **Essential** Master's degree in Data Science, Computer Science, Statistics, Mathematics, Physics, or a quantitative natural science; equivalent practical experience will also be considered.
• 1–4 years of relevant professional or research experience; internships, academic projects and thesis work may be considered.

How To Apply

Use the Apply now button and follow the instructions on the application page.