Senior Data Scientist, Multi-Omics AI for Targets

Pierre Fabre Laboratories; Toulouse; Hybride; EUR 70 000 - 110 000; Plein temps

Avantages offerts par ce poste

Profit-sharing

Shareholding with matching

Health insurance

Provident insurance

RTT & holidays 16 days + 25 personal

Public transport subsidies

Résumé du poste

Pierre Fabre Laboratories in Toulouse seeks a Senior Data Scientist to lead multi-omics AI initiatives for target identification and precision medicine. You will design AI systems learning from genomics, transcriptomics, and other omics data to uncover cancer vulnerabilities and translate discoveries into drug development.

The role collaborates with biologists and translational scientists, leveraging HPC/cloud environments and a strong emphasis on robust, interpretable AI frameworks across R&D.

Qualifications

• PhD or Master’s in Data Science, Computational Biology, or related field.
• 5+ years of data science experience in life sciences.
• Demonstrated multi-omics data analysis for target discovery/translational research.
• Hands-on experience building ML and DL models.

Responsabilités

• Develop ML/DL models to integrate multi-omics data for target prioritization.
• Identify molecular mechanisms and pathways across cancer indications.
• Support target discovery, toxicity prediction, and patient stratification.
• Design models using representation learning, self-supervised learning, and domain adaptation.
• Apply transformer-based architectures to biological/omics data.
• Ensure model robustness, reproducibility, and interpretability.

Connaissances

PhD or Master’s in Data Science/Comput

5+ years experience

Multi-omics analysis

Hands-on ML/DL

Formation

PhD in Data Science

Master’s in Computational Biology

Outils

Python

PyTorch

Transformers

HPC/Cloud

Description du poste

Pierre Fabre Laboratories in Toulouse seeks a Senior Data Scientist to lead multi-omics AI initiatives for target identification and precision medicine. You will design AI systems learning from genomics, transcriptomics, and other omics data to uncover cancer vulnerabilities and translate discoveries into drug development.

The role collaborates with biologists and translational scientists, leveraging HPC/cloud environments and a strong emphasis on robust, interpretable AI frameworks across R&D.