Senior Data Scientist - Graph

For our client we are looking for an ensemble profile that combines strong ML/data science foundations (core requirement) with depth in one or more of: graph-centric AI (graph theory, graph data science, knowledge graphs, GNNs such as GCN/GAT) and modern NLP/LLM engineering (semantic engineering, search/retrieval, RAG, personalization, text-to-SQL, and fine-tuning). The ideal candidate can translate research ideas into scalable architectures and well-designed workflows, and ship reliably in agile pods.

• Graph, Graph Data Science & Knowledge Graphs: graph theory fundamentals; graph analytics/graph data science; knowledge graph modeling (schemas/ontologies) and semantic layers; graph databases (e.g., Neo4j); graph embeddings; GNNs and graph ML tooling (e.g., GCN, GAT).
• NLP, LLMs & Semantic Engineering: modern NLP and language systems; semantic engineering; search/retrieval (lexical + vector + hybrid) and reranking; RAG; personalization; text-to-SQL; prompt engineering; fine-tuning/adaptation patterns.
• Core ML / Deep Learning / Data Science (Core Requirement): strong grounding in machine learning and deep learning; applied data science; ability to design experiments, evaluate models, and reason about quality, robustness, and bias.
• Hands-on Software Engineering & Delivery: solid, hands-on development experience (production Python and/or related stack); agile ways of working; R&D prototyping; architecture and workflow understanding and design across services, APIs, and data pipelines.