Job Description

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. The successful candidate will combine strong 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.

We are looking for someone who does not simply use AI tools, but knows how to build beyond them. The adecuado 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.

Accountabilities:





- Design and build data pipelines to support the development and deployment of machine learning models and advanced analytics.
- Build analytical software and AI solutions that integrate R&D-specific; data, models, and decision frameworks into intuitive user workflows.
- Build application containerization and orchestration within a cloud environment.
- Managing 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 strong 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.
- Ensure solutions are scientifically valid, explainable where needed, reliable in production, and aligned with data governance, model governance, security, and compliance expectations.

WL4A

📌 R&D Analytics - AI Developer (Ciudad de México)
🏢 Syngenta Seeds
📍 Ciudad de México