Role overview:

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.

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.

Core Technical responsibilities:

·       LLM Expertise Beyond Standard Usage

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.

·       Agentic AI & Autonomous Systems

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.

·       AGI-Aligned Research & Applications

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.

·       Mathematics & Quantitative Modeling

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.

·       Identifying & Modelling Growth Spaces

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.

Technical Skills & Experience (Required)

·       AI / ML Foundations

·       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).

·       LLM & Agentic AI

·       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.

·       Data Infrastructure

·       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).

·       Mathematics & Modelling

·       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.

·       Strategic Insight

·       Ability to translate technical findings into strategic opportunities.

·       Familiarity with innovation trend analysis, market research, and opportunity mapping.

Preferred / Nice-to-Have

·       Published research in AI, NLP, or AGI-related domains.

·       Contributions to open-source AI frameworks or libraries.

·       Experience in multi-modal AI (vision, speech, and text integration).

·       Familiarity with knowledge graph embeddings and semantic reasoning.

·       Exposure to reinforcement learning for autonomous decision-making.

·       Comfort working in fast-paced, experimental environments with evolving priorities.

• 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!