Senior Data Scientist, Descartes & Mauss

• We are seeking an exceptional Data Scientist with deep expertise in advanced AI systems, particularly in the design, deployment, and optimisation of Large Language Models (LLMs) beyond standard usage patterns
• The ideal candidate will have hands-on experience with Agentic AI architectures, a solid understanding of AGI-oriented research, exceptional mathematical foundations, and a keen ability to identify and model emerging growth spaces
• 5+ years in Data Science, Machine Learning, or Applied AI
• Strong understanding of NLP, deep learning architectures (Transformers, RNNs, etc.), and generative AI
• Proficiency with Python and ML frameworks (PyTorch, TensorFlow, JAX)
• Hands-on with open-source and proprietary LLMs (e.g., LLaMA, GPT, Claude, Mistral, Gemini, etc.)
• Experience building autonomous agents with frameworks like LangChain, CrewAI, AutoGPT, or custom architectures
• Familiarity with multi-agent coordination and reasoning frameworks
• Experience with graph databases (Neo4j, AWS Neptune) and vector stores (Pinecone, Weaviate, Milvus, FAISS)
• Strong SQL and NoSQL skills, and experience with data pipelines for large-scale ingestion and processing
• Understanding of distributed systems and cloud platforms (AWS preferred)
• Strong background in applied mathematics, statistical modelling, and optimisation
• Ability to create and validate complex models with explainability in mind
• Familiarity with Bayesian methods, time-series forecasting, and graph theory
• Ability to translate technical findings into strategic opportunities
• Familiarity with innovation trend analysis, market research, and opportunity mapping

Desirable

• You will work at the intersection of AI research, applied machine learning, and strategic data-driven opportunity discovery—helping shape solutions that push the boundaries of intelligent systems
• 1. Develop, fine-tune, and evaluate LLMs for specialised tasks (beyond prompt engineering)
• 2. Build retrieval-augmented generation (RAG) pipelines with scalable vector/graph data structures
• 3. Optimise models for performance, interpretability, and efficiency
• 4. Integrate LLMs with structured and unstructured data sources
• 5. Design and implement multi-agent systems capable of goal-oriented reasoning and long-term planning
• 6. Build dynamic tool-using agents that can orchestrate APIs, databases, and knowledge graphs autonomously
• 7. Research and implement advanced memory, planning, and decision-making frameworks for AI agents
• 8. Contribute to AGI-related experiments, focusing on reasoning, adaptability, and generalisation
• 9. Prototype cognitive architectures for open-ended problem-solving
• 10. Explore novel algorithms in reinforcement learning, self-improvement loops, and emergent behaviours
• 11. Apply advanced mathematical methods (linear algebra, optimisation, probability theory, graph theory, and statistics) to complex AI challenges
• 12. Design robust evaluation metrics for intelligent systems
• 13. Implement mathematical models for simulation, forecasting, and optimisation in AI pipelines
• 14. Use advanced analytics and AI-driven market mapping to identify high-potential domains
• 15. Develop predictive models for emerging trends using cross-domain datasets
• 16. Collaborate with strategy teams to translate AI insights into business and innovation roadmaps

• 30 mins chat with HR team
• 30 mins chat with the hiring manager
• A Case Study
• Office tour & meet the team (if you’re Paris based) - if your team is based in Paris, we’d love to invite you to our office for a quick tour and to meet them in person!