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# Data Scientist Confirmé - IA Générative - Assurance H/F

- **Company:** Anderson Rh
- **Location:** Paris, France (Remote available)
- **Experience:** Experienced
- **Contract:** Contract
- **Skills:** Agile Methodology, Artificial Intelligence, Cloud Computing, Continuous Integration, Python (Programming Language), NumPy, Tensorflow, Pytorch, Large Language Models, Generative AI, Pandas, Scikit Learn, HuggingFace, Machine Learning Operations
- **Published:** September 11, 2026
- **Apply:** https://www.hellowork.com/fr-fr/emplois/83173575.html

## About the Role

Poste en environnement sécurisé, méthodologie Agile, documentation en français.
Anglais professionnel obligatoire., Python avancé (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, Hugging Face)

 - Langchain/Langgraph
 - MLOps/LLMOps en production
 - PGVector
 - Gemini/Vertex AI et/ou Mistral (expérience de 6 ans minimum appréciée), Profil recherché8 ans d'expérience minimum en Data Science, dont une expérience significative en IA Générative/NLPPython avancé (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, Hugging Face)Langchain/Langgraph, MLOps/LLMOps en production, PGVectorExpérience de 6 ans minimum Gemini/Vertex AI et/ou Mistral appréciéeAutonomie, capacité à reprendre un existant rapidement, esprit d'analyseUne expérience secteur assurance/mutuelle est indispensable, 1. 8 ans d'expérience minimum en Data Science, dont une expérience significative en IA Générative/NLP
 2. Python avancé (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch, Hugging Face)
 3. Langchain/Langgraph, MLOps/LLMOps en production, PGVector
 4. Expérience de 6 ans minimum Gemini/Vertex AI et/ou Mistral appréciée
 5. Autonomie, capacité à reprendre un existant rapidement, esprit d'analyse
 6. Une expérience secteur assurance/mutuelle est indispensable

EUR

Anglais
Intelligence artificielle
Python
Autonomie
TensorFlow
Esprit d'analyse

## Description

Reprise et continuité d'un projet IA Générative existant

 - Conception, entraînement et optimisation de LLM (Gemini/Vertex AI, Mistral)
 - Optimisation d'un pipeline RAG en production (PGVector)
 - Mise en oeuvre de bonnes pratiques MLOps/LLMOps (CI/CD IA, monitoring, industrialisation)
 - Déploiement sur environnement Cloud (Cloud Run)
 - Collaboration étroite avec les équipes techniques, Product Owners et architectes

Outils & Environnement


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