Sr. Data Scientist, Retrieval & Agentic Systems
Summary
Senior Data Scientist in Madrid (hybrid, 3 days/week) who owns and advances content discovery in Kharon's Corestream risk-intelligence product — improving hybrid search (BM25, embeddings, reranking, graph signals), building agentic search and narrative generation. Core tools: Python, LLM/RAG pipelines, retrieval evaluation.
- Own and advance content discovery in Corestream, improving how customers find, explore, and engage with relevant risk intelligence, while helping shape the technical direction of discovery capabilities across Kharon's products.
- Redesigning and improving Kharon's hybrid search capabilities, opening new avenues for content discovery — drawing on modern approaches including BM25 tuning, semantic distance, RAG, reranking, and graph-based signals that go beyond traditional semantic similarity.
- Explore and build new discovery experiences, including agentic search, intelligent content categorization, personalized feeds, and systems that surface relevant connections across large collections of intelligence.
- Driving the evolution of Kharon's narrative generation pipeline — how we take graph and analytical data and transform it into compelling, accurate written intelligence for customers.
- Own problems end to end, from identifying opportunities and evaluating approaches to prototyping, implementing, deploying, and continuously improving production systems.
- Collaborate closely with Product, Data Science, and Engineering, influencing priorities and architectural decisions while maintaining a strong hands-on contribution.
- A proven interest or experience in global security, financial crimes, sanctions, export controls, or related domains
- 3+ years of experience in Data Science, AI Engineering, ML Engineering, or related software engineering roles
- Hands-on experience in information retrieval, including experience with some combination of lexical search, embeddings, vector search, hybrid retrieval, ranking, reranking, and retrieval evaluation
- Practical experience building LLM-powered applications, RAG pipelines, or agentic workflows.
- Strong Python skills and the software engineering ability to take an idea from prototype to a reliable, maintainable production implementation
- Demonstrated initiative and technical ownership — a track record of tackling ambiguous problems independently, challenging assumptions, and driving solutions forward while working with stakeholders
- Curiosity and a willingness to experiment with emerging approaches while maintaining a pragmatic focus on what actually works
- Bonus: Experience with graph-aware search or integrating knowledge graph outputs into retrieval systems
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