Data scientist - Stage - H/F

Who we are ? We are Pierre Fabre Laboratories, a global leader combining pharmaceutical expertise with dermo-cosmetics to support consumers and patients at every stage of their care journey. Our portfolio includes several medical franchises and international brands such as Eau Thermale Avène, Ducray, A-Derma, Klorane, René Furterer, and Pierre Fabre Oral Care. By joining us, you become part of a meaningful company where the human dimension is essential. You become a participant in the "We Care Movement," a movement that values excellence and innovation within passionate teams. Together, we push the boundaries of science to unite health and beauty for the benefit of all, because Every time we care for a single person, we make the whole world better. Present in 120 countries with a team of over 10,000 employees, we are proud to create a scientific and human impact, today and tomorrow! If caring is at the heart of your values, join Pierre Fabre Laboratories and become a key player in the “We Care Movement". Your mission Votre rôle au sein d’une entreprise pionnière en pleine expansion. Avec l'avènement des technologies de séquençage unicellulaire à haut débit, de vastes quantités de données de cellules individuelles sont désormais disponibles pour de nombreux tissus et conditions biologiques. L'objectif de ce stage est d'explorer la valeur ajoutée des scFMs dans les applications de découverte de médicaments. Les scFMs permettent de nombreuses applications, notamment :

• l'intégration de lots de données (batch integration) ;
• l'annotation des types cellulaires ;
• la prédiction des effets de perturbations biologiques. Nous souhaitons évaluer les performances de ces modèles sur différentes étapes du processus de découverte de médicaments afin d'identifier leurs points forts et leurs limites. Dans le cadre de ce stage, vos principales missions seront :
• Concevoir et mettre en œuvre un benchmark pour évaluer les modèles de fondation appliqués aux données unicellulaires (scFMs).
• Présenter et communiquer les résultats aux équipes de Data Science. Les plus du stage
• Missions diversifiées
• Gratification attractive
• Possibilité de réaliser un jour de télétravail par semaine après un mois d'intégration
• Dotations produits
• Restaurant d'Entreprise ou Titres Restaurants Le stage se déroulera sur notre site Langlade, à Toulouse (31) et débutera à partir de février 2027 pour une durée de 6 mois. Merci d’indiquer vos dates de disponibilités dans votre CV. Une lettre de motivation sera grandement appréciée. # Young People __________________________________________________________________________ Your role within a pioneering, rapidly expanding company. With the advent of high-throughput single-cell sequencing technologies, vast amounts of single-cell data are now available across a wide range of tissues and biological conditions. The objective of this internship is to explore the value of single-cell foundation models (scFMs) for drug discovery applications. scFMs support a broad range of applications, including:
• Batch integration;
• Cell type annotation;
• Prediction of biological perturbation effects. We aim to assess the performance of these models across different stages of the drug discovery process in order to identify their strengths and limitations. In this internship, your main responsibilities will be:
• Design and implement a benchmark framework to evaluate single-cell foundation models (scFMs);
• Present and communicate results to Data Science teams. Benefits
• Diverse and impactful assignments;
• Competitive internship allowance;
• Opportunity to work remotely one day per week after the first month of onboarding;
• Company product allocation;
• Company restaurant or meal vouchers. The internship will take place at our Langlade site in Toulouse (31), France , and is expected to start from February 2027 , up to 6 months. Please indicate your availability dates in your

Cv. a

cover letter is strongly encouraged. Who you are ? Vos compétences au service de projets novateurs. Nous recherchons un(e) étudiant(e) en dernière année de formation (Master 2, école d’ingénieurs) pour un stage de fin d'études, disposant de solides compétences en Data Science, d’une bonne compréhension de la biologie et d’un fort intérêt pour les données unicellulaires ( single-cell ). Compétences requises

• Maîtrise de Python ;
• Expérience en apprentissage automatique ( Machine Learning ) et des bibliothèques Python associées (Scikit-learn, PyTorch, etc.) ;
• Bonnes capacités de communication et de présentation ;
• Maîtrise du français et de l’anglais, à l’écrit comme à l’oral. __________________________________________________________________________ Put your skills to work on innovative projects. We are looking for a final-year internship student (master's degree, engineering school) with advanced skills in data science, with good understanding of biology and a strong interest in single-cell data. Skills
• Proficiency in Python
• Experience in machine learning, and its python libraries (scikit-learn, pytorch, ...)
• Good communication and presentation skills
• Fluency in French and English At Pierre Fabre Laboratories, we believe that our greatest asset is our people. We are committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee based on race, color, religion, creed, national origin or ancestry, sex, sexual orientation, gender identity or expression, age, physical or mental disability, veteran or military status, genetic information, or any other legally recognized protected basis under federal, state, or local law. The information collected by this application is solely to determine suitability for employment, verify identify, and maintain employment statistics on applicants. Applicants with disabilities may be entitled to reasonable accommodation under the Americans with Disabilities Act and certain state or local laws. Please inform the company’s personnel representative if you need assistance completing this application or to otherwise participate in the application process. Thus, we commit to considering all applications equally, without fail.