Knowledge Graph Engineering, Financial Risk Data
Knowledge Graph | Semantic Modelling | Graph Databases | Credit Risk | AI
Head of Knowledge Graph Engineering - Data Science and Analytics
London (hybrid)
Up to £200k + B&B
This is a knowledge graph engineering, semantic modelling, graph query design, risk data, graph-grounded AI role. Knowledge graph engineers, data engineers, applied data scientists or semantic technologists with a knowledge graph or connected data application built for analytics, content or AI, taken from domain conversations to a working implementation. You will be tasked with owning what a knowledge graph is in a new data science and analytics organisation: semantic model, data mappings, queries, provenance and temporal rules, validation with domain experts, and the first working implementation.
THE ROLE:
- Graph build: initial graph models, data mappings, queries and working implementations, built personally from a live product problem and released to users
- Languages and tools: Python, SQL, Cypher, SPARQL, RDF, property graphs; no single stack required
- Semantic model: analytical domains, subjects, capabilities, methods, inputs, outputs and the relationships between them
- Relationship modelling: identity, time, provenance, validation; meaning, timing, source and limits of each relationship stated
- Risk pathways: country and industry developments linked to companies, exposures and credit outcomes
- Graph and AI: connected retrieval, grounded answers, traceable sources, answer quality assessed against real questions
- Model testing: graph relationships, features and algorithms tested with data scientists against a simpler baseline
- Content enrichment: tagging with research and content teams linking documents, topics, entities, events and analytical capabilities; source and context preserved
- Delivery: ingestion, validation and interfaces with engineering and platform teams; shared entity and identifier services; entity-linking requirements; reusable patterns; teaching partner teams
Own the first implementation: a working graph for one priority risk or analytics question, on real data, with a model validated by domain experts and prospective users. Own the test: at least one adjacent use case, such as graph-derived features for a model or connected research for source-backed AI, measured against a simpler approach. Own the patterns: modelling patterns, working examples and an adoption plan for other teams.
#J-18808-Ljbffr
Director of Ontology Knowledge Engineering in London employer: Intelix.AI
Join a leading global AI and analytics firm that champions innovation at the intersection of knowledge graphs and generative AI. With a strong emphasis on employee growth, you will have the opportunity to work in a dynamic, hybrid environment that fosters collaboration and creativity while tackling high-impact projects for prestigious clients. Enjoy competitive compensation, meaningful equity options, and a culture that values autonomy and continuous learning, making it an exceptional place for data professionals to thrive.
