Syngenta Group: Senior Data Scientist - AI and GenAI — London, United Kingdom

About Syngenta Group

Syngenta Group works in agricultural technology and crop-related innovation. Its employer descriptions connect scientific development and crop solutions with helping farmers use resources effectively and improve how crops are grown. Material from its vegetables-and-flowers business also identifies seed breeding for different climates and horticultural needs. Across these activities, the stated business purpose links agricultural production with resource use and environmental concerns, bringing scientific and technological work to practical farming and growing conditions.

The position and its setting

Syngenta Group lists Senior Data Scientist - AI and GenAI in London, United Kingdom, under reference 18075317. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Department: Development
• Employment type: Full-time
• Employer listing date: 22 October 2026
• Brands: Syngenta Crop Protection

Responsibilities and work described

• 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
• 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
• Development: 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
• Integration: 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
• Collaboration: with R&D IT and software development teams to build data connections and deploy analytical, AI-enabled or customer-facing applications
• Quality / delivery: the responsible, transparent and secure use of AI and GenAI, including appropriate model validation, documentation, reproducibility, data governance and risk management
• Establishment: 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
• Leadership: 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

Essential requirements and candidate background

• Essential
• Demonstrated experience developing machine-learning, AI or Generative AI-enabled predictive models for biological, scientific, agricultural, life-science or Crop Protection-related outcomes
• Knowledge: 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
• Understanding: model validation, robustness assessment and the interpretation of predictive-model performance, including the reliability, limitations and risks associated with AI and GenAI solutions
• 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: 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
• Analytical thinking, creative problem-solving and intellectual curiosity
• Work independently, manage priorities effectively and deliver work to a high standard of accuracy
• Communication skills, including data visualisation and storytelling for specialist and non-specialist audiences
• Professional proficiency in English
• Academic or professional experience in biology, chemistry, ecology, environmental science, biokinetics or related scientific domains
• Experience working with laboratory, field-trial, environmental or other complex scientific datasets
• Experience: interdisciplinary or multiscale modelling approaches
• Experience building, evaluating or deploying AI/GenAI applications in scientific, R&D or regulated environments
• Experience collaborating with external partners, vendors, academic institutions, or R&D IT teams on data platforms, AI solutions and analytical applications
• Key Capabilities
• Adaptability across different scientific questions, data types, analytical methods, AI techniques and Generative AI applications

Education and qualification routes

• Degree / qualification: Advanced academic background in Data Science, Machine Learning, Statistics, Mathematics, Physics or a related quantitative discipline; postgraduate education is strongly preferred

Preferred experience and additional strengths

• Preferred

Location, schedule and contract conditions

For this overseas or regional listing, confirm work authorisation and any location restrictions with the employer. The location does not establish visa sponsorship or permission to work remotely from India.

• Work arrangement: On-site
• Advertised location: London, United Kingdom

Benefits mentioned by the employer

• 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

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Senior Data Scientist - AI and GenAI vacancy is not an offer for reference 18075317; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Syngenta Group

Applications follow the official employer page linked below.

Open the official Syngenta Group posting below and match the title and reference 18075317.

Compare your qualifications and experience with the essential conditions for the London, United Kingdom position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• AI/ML
• LLMs
• machine learning
• AI
• laboratory testing
• chemistry
• audit
• risk management
• sustainability
• stakeholder management
• agriculture