Senior Data Scientist - AI and GenAI

Responsibilities

Develop and validate predictive models and AI/GenAI solutions using scientific and experimental data to support Crop Protection research, discovery, and business decisions. Collaborate with scientists and technical teams to deploy scalable, responsible analytical applications, communicate insights, mentor junior colleagues, and guide AI initiative priorities.

Requirements

An advanced quantitative academic background, preferably postgraduate, and demonstrated experience developing machine-learning, AI, or GenAI predictive models are required, along with strong Python and practical LLM, prompt engineering, RAG, and evaluation experience. Candidates should be skilled in model validation, responsible AI, complex data analysis, cross-functional collaboration, and communicating insights; scientific-domain experience is preferred.

Skills

• Data Science
• Machine Learning
• Generative AI
• Python
• Predictive Modeling
• Large Language Models
• Prompt Engineering
• Retrieval-Augmented Generation
• Model Validation
• Data Analysis
• Scientific Data Modeling
• Responsible AI
• Data Governance
• Data Visualization
• Stakeholder Communication
• Team Leadership

Benefits

• Pension Scheme
• Bonus Scheme
• Private Medical Insurance
• Life Insurance
• Annual Leave
• Flexible Working
• Training and Development

About this role

Company Description
Bracknell, UK Employees can work remotely
Full-time
Company Description
Syngenta Crop Protection is a leader in agricultural innovation, bringing breakthrough technologies and solutions that enable farmers to grow productively and sustainably. We offer a leading portfolio of crop protection solutions for plant and soil health, as well as digital solutions that transform the decision-making capabilities of farmers. Our 17,900 employees serve to advance agriculture in more than 90 countries around the world. Syngenta Crop Protection is headquartered in Basel, Switzerland, and is part of the Syngenta Group.
Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. No matter what your position, you will have a vital role in safely feeding the world and taking care of our planet. Join us and help shape the future of agriculture.
Job Description
Join our global analytics team within Product Biology & Sustainable Innovation and help shape the use of predictive science in Crop Protection R&D. As a Senior Data Scientist – Predictive Science, you will develop machine-learning and predictive-modelling approaches that guide the discovery, optimisation and development of Crop Protection solutions. You will connect diverse scientific data with business and R&D decision-making, working closely with multidisciplinary teams to generate robust insights and accelerate complex decisions. This role offers the opportunity to work autonomously on high-impact scientific challenges, influence modelling approaches and data strategy, and collaborate with experts across an international research environment.
What you will do
Develop and validate predictive models based on historical, experimental and scientific data to support project and business decisions.
Apply AI, machine learning and Generative AI methods to accelerate scientific discovery, improve decision-making and create scalable solutions across Crop Protection R&D.
Identify, assess and prioritise AI and GenAI use cases based on scientific relevance, business value, feasibility, data readiness, risk and expected impact.
Partner with multidisciplinary teams to support the discovery, optimisation and development of Crop Protection leads and product candidates.
Integrate and analyse data from different sources, including physical chemistry, biokinetic, environmental, laboratory, glasshouse and field-trial data.
Work with scientists to understand complex problems and identify opportunities where predictive modelling, AI or GenAI can create measurable value.
Collaborate with R&D IT and software development teams to build data connections and deploy analytical, AI-enabled or customer-facing applications.
Ensure the responsible, transparent and secure use of AI and GenAI, including appropriate model validation, documentation, reproducibility, data governance and risk management.
Establish and track KPIs, model performance measures and success metrics for predictive modelling and AI initiatives to demonstrate scientific and business impact.
Communicate complex analytical and AI-driven insights to non-technical stakeholders through clear visualisations, practical recommendations and executive summaries.
Lead and mentor junior data scientists and analysts, fostering a culture of continuous learning and knowledge sharing around AI and machine-learning best practices.
Stay abreast of emerging AI, machine-learning and GenAI technologies, methodologies and industry developments to ensure the organisation remains at the forefront of predictive science innovation.
Develop and maintain comprehensive documentation of models, methodologies and analytical frameworks to ensure reproducibility, auditability and knowledge transfer across the team.
Optimise model performance and scalability, ensuring analytical solutions can handle increasing data volumes and computational demands across the R&D portfolio.
Manage stakeholder relationships and provide strategic guidance on the prioritisation and sequencing of AI and predictive-modelling initiatives aligned with business objectives.
Qualifications
Essential
Advanced academic background in Data Science, Machine Learning, Statistics, Mathematics, Physics or a related quantitative discipline; postgraduate education is strongly preferred.
Demonstrated experience developing machine-learning, AI or Generative AI-enabled predictive models for biological, scientific, agricultural, life-science or Crop Protection-related outcomes.
Strong knowledge of Python and relevant data science, analytics, modelling, AI and Generative AI tools and frameworks.
Practical experience with Generative AI, including Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and the evaluation of GenAI outputs.
Strong understanding of model validation, robustness assessment and the interpretation of predictive-model performance, including the reliability, limitations and risks associated with AI and GenAI solutions.
Ability to assess and select the most appropriate solution approach-such as predictive modelling, conventional machine learning, automation or Generative AI-based on the problem, data availability, feasibility, expected value and risk.
Knowledge of responsible AI principles, including data privacy, security, explainability, bias awareness, governance and human oversight.
Experience analysing complex datasets and translating findings into clear, actionable insights.
Experience collaborating with scientists, data scientists, software engineers or technical specialists to solve complex problems and deploy AI-enabled solutions.
Strong analytical thinking, creative problem-solving and intellectual curiosity.
Ability to work independently, manage priorities effectively and deliver work to a high standard of accuracy.
Strong communication skills, including data visualisation and storytelling for specialist and non-specialist audiences.
Professio...

About Syngenta Group

Farming · 10,001+ employees · headquartered in Basel, Basel-Town