Data Scientist II GenAI

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.

Qualifications :

**Essential**

• Masters degree in Data Science Computer Science Statistics Mathematics Physics or a quantitative natural science; equivalent practical experience will also be considered.
• 14 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 AWS or Azure and basic deployment of ML models or data pipelines.
• Background or interest in biology chemistry environmental science Crop Protection or other life-science domains.

Additional Information :

Site Details 

Jealotts 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 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 

• Extensive benefits package including a generous pension scheme bonus scheme private medical & life insurance. 

• Up to 31.5 days annual holiday. 

• Flexible working 

• We offer a position which contributes to valuable and impactful work in a stimulating and international environment. 

• The opportunity to develop and apply your science within the chemical industry. 

• The chance to work as part of a global team to address the current and future needs of the agricultural sector. 

• Learning culture and wide range of training options. 

Syngenta has been ranked as a top 5 employer and number 1 in agriculture by Science Journal.  

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. Learn more about our D&I initiatives here:  Work :

No

Employment Type :

Full-time