Biostatistician - AI-Driven Breeding Analytics

LIMAGRAIN; Chappes; Sur place; EUR 80 000 - 110 000; Plein temps

Résumé du poste

Limagrain is seeking a data science-focused professional to design AI-based decision-support tools for global breeding programs at its Chappes research center in France. You will integrate biostatistics, quantitative genetics and data science to leverage genomic, phenotypic and environmental data.

You will collaborate with breeding, genetics, IT and data science teams in an international environment, contributing to innovative AI-driven platforms and reproducible analytics practices.

Qualifications

• Strong statistical and analytical background required.
• Experience or strong interest in AI, Generative AI and LLMs.
• Advanced programming in R; Python experience highly desirable.
• Experience developing analytical tools, applications or decision-support systems.

Responsabilités

• Design and deploy AI and Generative AI solutions supporting breeding decisions.
• Develop LLM-based assistants, knowledge discovery tools and advanced analytics platforms.
• Build scalable analytical modules and dashboards for breeding programs.
• Develop innovative biostatistical and AI-driven approaches for large-scale datasets.
• Contribute to modern data platforms and scalable environments using R, Python, Git and Conda.
• Support the deployment of MLOps and LLMOps practices.

Connaissances

Statistical analysis

Biostatistics

Data Science

Advanced R

Python

Formation

MSc or PhD in Statistics or related field

Outils

Git

Conda

Description du poste

Limagrain is seeking a data science-focused professional to design AI-based decision-support tools for global breeding programs at its Chappes research center in France. You will integrate biostatistics, quantitative genetics and data science to leverage genomic, phenotypic and environmental data.

You will collaborate with breeding, genetics, IT and data science teams in an international environment, contributing to innovative AI-driven platforms and reproducible analytics practices.