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# Senior Data Scientist - Multiomics ai & Target Identification - CDI H/F

- **Company:** The Data Science & Biometry Department Within Pierre Fabre R&d Medical Care
- **Location:** Toulouse, France
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Cloud Computing, Python (Programming Language), Machine Learning, Pytorch, Deep Learning
- **Published:** August 5, 2026
- **Apply:** https://www.hellowork.com/fr-fr/emplois/82046069.html

## About the Role

Your skills at the service of innovative projects:

Education

 - PhD or Master's degree in Data Science, Computational Biology, or a related field.

Experience

 - 5+ years of experience in data science applied to life sciences.
 - Demonstrated experience in multiomics data analysis for target discovery or translational research.
 - Handson experience developing machine learning and deep learning models.

Technical Skills

 - Strong proficiency in Python.
 - Experience with deep learning frameworks (e.g., PyTorch).
 - Solid knowledge of modern ML approaches, including transformers, representation learning, selfsupervised learning, and domain adaptation.
 - Familiarity with HPC and/or cloud computing environments.
 - Strong grounding in bioinformatics, cancer biology, and pathway-level reasoning.

Soft Skills & Mindset

 - Strong analytical and structured thinking.
 - Ability to translate biological, scientific, and strategic challenges into effective AI solutions.
 - Excellent crossfunctional collaboration skills.
 - High intellectual curiosity and innovation mindset.
 - Professional proficiency in English (written and oral).

## Description

Develop and apply machine learning and deep learning models to integrate multiomics data for target identification and prioritization.

 - Contribute to biological and disease understanding by identifying molecular mechanisms, pathways, and vulnerabilities across cancer indications.
 - Support diverse downstream assessments, including target discovery, toxicity prediction, and patient stratification.

 - Design and train advanced models leveraging representation learning, selfsupervised learning, and domain adaptation.
 - Apply and adapt transformerbased architectures to biological and omics data.

 - Ensure strong standards for model robustness, reproducibility, interpretability, and scientific validity.
 - Work with HPC and/or cloud environments to scale model training and experimentation.
 - Collaborate closely with biologists, translational scientists, and drug discovery teams to translate biological questions and hypotheses into AIdriven solutions.
 - Contribute to shared AI frameworks and reusable methodological components across R&D.


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