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Senior Data Scientist
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Senior AI Data Scientist, Knowledge Graph
At-a-Glance
- Senior hire, building a knowledge graph capability from the ground up rather than maintaining someone else's
- Neo4j, GraphRAG, Python and LLM-based document processing
- Hybrid working, Belfast
- Up to £100,000 plus share options
- Sovereign AI applied to secure, high-consequence environments
About the Company
A sovereign AI company headquartered in Belfast, building systems that let organisations apply modern AI to their most sensitive data without it leaving their control. Its work spans defence, government and heavily regulated industry, where the usual cloud-first approach is not an option. The engineering team is small, technically deep and growing, and the problems are the kind that have no off-the-shelf answer.
The Role
This is the founding technical hire for the company's knowledge graph work. You will own the pipeline that turns large, messy, unstructured document estates into structured graph representations that AI systems can reason over reliably, and you will own the evaluation frameworks that prove it is actually working. You will sit close to the CTO and to customers, translating real operational problems into ontology and retrieval design decisions. This suits someone who has done this at depth before... not someone who has read about GraphRAG and wants to try it. Expect autonomy, ambiguity and very little process.
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Key Responsibilities
- Own the document-to-graph pipeline end to end, covering ingestion, entity and relationship extraction, entity resolution and enrichment
- Design domain ontologies and schemas in partnership with subject matter experts and customers
- Build GraphRAG retrieval systems that combine graph traversal with LLM reasoning
- Define and run evaluation frameworks measuring extraction accuracy, retrieval quality and answer faithfulness
- Establish the benchmarks and regression testing that keep model and pipeline changes honest over time
- Work with engineering to deploy graph and retrieval workloads into controlled and air-gapped environments
- Translate customer problems into graph modelling decisions, and defend those decisions to technical and non-technical audiences
- Embed AI tooling into your own daily workflow and set the standard for how the wider team uses it
- Mentor engineers as the graph capability grows around you
What You'll Need
Essential:
- 6+ years in data science, machine learning or ML engineering, with a substantial portion of that spent on graph-based systems
- Demonstrable experience turning unstructured documents into structured graph representations at scale, not as a side project
- Hands-on Neo4j and Cypher, or a comparable graph database such as Neptune or TigerGraph
- Proven experience designing and running evaluation frameworks for extraction and retrieval quality, with the metrics to show for it
- Strong Python, and comfort owning production code rather than handing it over
- Practical experience with LLM pipelines, embeddings and retrieval architectures
- Willingness and eligibility to obtain UK security clearance
- Right to work in the UK
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Desirable / Nice to Have:
- Exposure to defence, government or other regulated and high-sensitivity data environments
- Semantic web and ontology engineering experience across RDF, OWL or SPARQL
- Published work or open-source contributions in graph machine learning
Why Apply?
- Salary up to £100,000, plus share options
- Hybrid working from Belfast, with genuine flexibility rather than a badge-in policy
- Founding ownership of the knowledge graph capability, including the tooling, the ontology and the evaluation approach
- Problems that are genuinely unsolved, applied to work that matters rather than to another dashboard
- Direct access to the CTO and to customers, with no layers of management in between
- A team that actively expects AI tooling in the daily workflow rather than restricting it
- Room to grow into technical leadership as the function scales
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