Syngenta Group: R&D Analytics - AI Developer — São Paulo, SP, Brazil

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 R&D Analytics - AI Developer in São Paulo, SP, Brazil, under reference 18074915. 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: 16 October 2026
• Brands: Syngenta Seeds

Responsibilities and work described

• 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 Analytics & Data Sciences Team is seeking an R&D Analytics - AI Developer
• This role will build software solutions that enable R&D teams to analyze experimental, phenotypic, genotypic, environmental, operational, and pipeline data at scale
• Combine software engineering, applied AI, and scientific data understanding to deliver tools that improve decision-making across Vegetable R&D
• The role requires fluency in modern AI-assisted development tools, including internal AI platforms, while also being able to design solutions that go beyond generic AI by embedding R&D-specific models, datasets, workflows, and domain logic
• Someone who does not simply use AI tools, but knows how to build beyond them
• The ideal candidate will use AI platforms and modern AI development assistants to accelerate delivery, while also creating differentiated R&D solutions that depend on Syngenta’s scientific data, models, experimental context, and domain expertise
• Design: and build data pipelines to support the development and deployment of machine learning models and advanced analytics
• Building: analytical software and AI solutions that integrate R&D-specific data, models, and decision frameworks into intuitive user workflows
• Building: application containerization and orchestration within a cloud environment
• Management: software deployment and configuration in non-production and production environments
• Partner with R&D scientists, data scientists, and analytics teams to translate scientific and operational needs into scalable software solutions
• Use AI-assisted software development tools to improve productivity, code quality, documentation, testing, while maintaining engineering judgment and accountability for final outputs
• Evaluate emerging AI, agentic AI, and automation capabilities and identify where they can responsibly improve R&D analytics, experimentation, and decision support
• Quality / delivery: solutions are scientifically valid, explainable where needed, reliable in production, and aligned with data governance, model governance, security, and compliance expectations
• WL4A

Essential requirements and candidate background

• Master or PhD in Data Science, Artificial Intelligence, Computer Science, Quantitative Genetics, or a related field
• A minimum of 5 years of professional software engineering experience, with at least 3 years building and maintaining production systems in Python
• Familiarity: AI and machine learning frameworks
• Knowledge and experience of Agentic AI and large language models
• Experience designing, building, and maintaining end-to-end software solutions
• Python programming and software engineering skills
• Experience: big data platforms and technologies (e.g., Snowflake, Databricks)
• Experience packaging, deploying, and monitoring ML models in production
• Proficiency: developing cloud-native applications and deploying to cloud environments, especially AWS
• Experience: API technologies (e.g. RESTful APIs or GraphQL)
• Experience developing software products or analytical applications for scientific, R&D, agricultural, biological, or similarly complex data domains
• Use AI coding assistants, enterprise AI platforms, or agentic AI tools to accelerate software delivery while applying independent technical review, testing, and validation
• Familiarity: responsible AI practices, including model evaluation, bias/error assessment, traceability, prompt/version management, and human-in-the-loop workflows

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.

• You may work from any site in Brazil following the local guidances of the hybrid model
• #LI-DNI
• Work arrangement: Hybrid
• Advertised location: São Paulo, SP, Brazil

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another R&D Analytics - AI Developer vacancy is not an offer for reference 18074915; 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 18074915.

Compare your qualifications and experience with the essential conditions for the São Paulo, SP, Brazil 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
• AWS
• APIs
• AI/ML
• LLMs
• machine learning
• AI
• compliance
• agriculture
• API development