Senior Data Scientist – Multi‑omics AI & Target Identification - CDI

Pierre Fabre is the 2nd largest dermo-cosmetics laboratory in the world, the 2nd largest private French pharmaceutical group and the market leader in France for products sold over the counter in pharmacies.

Its portfolio includes several medical franchises and international brands including Pierre Fabre Oncologie, Pierre Fabre Dermatologie, Eau Thermale Avène, Klorane, Ducray, René Furterer, A-Derma, Naturactive, Pierre Fabre Oral Care. 

Established in the Occitanie region since its creation, and manufacturing over 95% of its products in France, the Group employs some 10,000 people worldwide. Its products are distributed in about 130 countries. 86% of the Pierre Fabre Group is held by the Pierre Fabre Foundation, a government-recognized public-interest foundation, while a smaller share is owned by its employees via an employee stock ownership plan. 

In 2019, Ecocert Environment assessed the Group’s corporate social and environmental responsibility approach in accordance with the ISO 26000 sustainable development standard and awarded it the “Excellence” level.

Pierre Fabre is recognized as one of the "World's Best Employers 2021" by Forbes. Our group is ranked in the Top 3 in the cosmetics industry and in the Top 10 in the pharmaceutical industry worldwide. 

We are seeking in Toulouse (Oncopole- Langlade Location) a talented and highly motivated Senior Data Scientist - Multi‑Omics AI & Target Identification with strong expertise in multi‑omics analysis, machine learning, and AI for biological data to accelerate target identification, translational research, and precision medicine strategies.

As a key member of the Data Science & Biometry Department within Pierre Fabre R&D Medical Care, you will design and develop AI systems capable of learning from high‑dimensional biological data (genomics, transcriptomics, proteomics, functional screens) to deepen disease understanding, uncover pathway‑level mechanisms, and reveal cancer vulnerabilities across indications.

This role sits at the interface of AI research, computational biology, and drug discovery, and is embedded within the Methods & Innovation team, responsible for cross‑functional initiatives involving advanced AI methodologies.

Your role within a pioneering company in full expansion:

Key Responsibilities

• Develop and apply machine learning and deep learning models to integrate multi‑omics 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, self‑supervised learning, and domain adaptation.
• Apply and adapt transformer‑based 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 AI‑driven solutions.
• Contribute to shared AI frameworks and reusable methodological components across R&D.

This position is based in Toulouse with flexibility for remote work.

We offer an attractive remuneration/benefits package: Incentives, profit-sharing, Pierre Fabre shareholding with matching contribution, health and provident insurance, 16 days of holidays (RTT) in addition to 25 days of personal holidays, public transport participation.

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 multi‑omics data analysis for target discovery or translational research.
• Hands‑on 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, self‑supervised 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 cross‑functional collaboration skills.
• High intellectual curiosity and innovation mindset.
• Professional proficiency in English (written and oral).

We are convinced that diversity is a source of fulfillment, social balance and complementarity for our employees, which is why our offers are open to all, without restriction.