Data Scientist (Paris)

**Job title:*

• Data Scientist
• Location: Paris
• Hybrid: 60% office, 40% remote
• Permanent: Fulltime
• Work with business teams to understand requirements, and translate them into technical needs
• Apply data science expertise in machine learning, statistics, forecasting and optimization to multiple AI projects/products
• Gather/organize large & complex data assets, and perform relevant analysis to propose and implement relevant data models for each business case
• Build models, algorithms, simulations, and experiments by writing highly optimized code and using state-of-the art machine learning technologies
• Work on full-spectrum of activities, from conducting ML experiments to delivering production-ready models
• Use data analysis, visualization, storytelling, and data technologies to scope, define and deliver AI-based data products
• Work with developers, engineers, and MLOps to deliver AI/ML solutions
• Experience developing deployable code and deploying models in product-focused development under an agile environment
• Experience in production-ready software development
• Track record of applying machine learning/deep learning approaches to solve health-related problems
• Experience with content generation using Large Language Models (LLM) is desired
• Experience in a healthcare company is a strong plus
• Excellent written and verbal communication skills
• Experience working with multiple teams to drive alignment and results
• Service-oriented, flexible, positive team player
• Self-motivated, takes initiative
• Problem solving & critical thinking
• Experience with Retrieval-Augmented Generation (RAG)
• Designing and implementing RAG pipelines to enhance LLM capabilities with external knowledge
• Fine-tuning models to combine retrieval results with generative outputs
• Knowledge of Agentic Frameworks
• Building systems where LLMs act as agents capable of reasoning and interacting with APIs/tools
• Familiarity with frameworks like Lang

Chain, or similar agent-driven architectures

• Prompt Engineering and Few-Shot Learning
• Crafting effective prompts for specific tasks, leveraging few-shot and zero-shot paradigms
• Optimizing prompts for performance, clarity, and reduced token consumption
• Familiarity with LLM evaluation frameworks (e.g., Open

AI Evals, custom metrics)

• Natural Language Processing (NLP) Fundamentals
• Understanding of transformer-based architectures (e.g., GPT, BERT, T5)
• Knowledge of tokenization, embeddings, and pre-training/fine-tuning paradigms
• Building NLP pipelines for text classification, summarization, and entity recognition
• Familiarity with Knowledge Graphs and Semantic Search
• Building and integrating knowledge graphs for structured information retrieval
• Leveraging semantic search for improved accuracy in large-scale document understanding

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