Freelance Data Scientist with Ontology and Graph expertise

adesso Belgium is looking for a data scientist (2) to support our customer in building an AI Agent Platform Data Foundations.

Our customer is an international, independent provider of indirect procurement services.

Build the data and context layer that powers the customer's procurement AI agent platform — canonical models, knowledge graph, semantic/taxonomy layer, and the retrieval and evaluation pipelines agents depend on. This is production infrastructure from day one, not a proof of concept: versioned, tested, and built for multiple tenants.

You will work on:

• Data products & canonical modeling — Design and deliver the first reference data products (Supplier Master, Spend Cube, Contract Register) end to end, from source ingestion through the canonical model.

• Knowledge graph & ontology — Stand up and operate the platform and working graphs (ontology, reference data, tenant separation); own the graph-plus-vector retrieval design, deciding per source whether data is represented as entities/relationships, embeddings, or both.

• Master data & semantic layer — Build matching rules, golden records, and stewardship processes for Suppliers, Contracts, and Categories; contribute to the business glossary and taxonomy-to-ontology mapping.

• Extraction & evaluation — Build document-extraction pipelines (e.g., from SharePoint sources) with confidence scoring and human review for uncertain cases; build and maintain labelled evaluation datasets and accuracy benchmarks against agreed thresholds.

• Connector framework — Build source connectors (starting with Fabric data products and master data golden records) so new sources can be onboarded without modifying the graph core or query service.

• Quality feedback loop — Implement the mechanism routing agent overrides, failures, or low-confidence retrievals back to data owners so data products improve over time rather than decay.

• Engineering practice — Work exclusively in the GitHub/Azure environment; ship via pull request with CI (build, tests, security scan, evaluation fixtures); version all schemas, connector contracts, and extraction configs.

• Demonstrated experience taking a knowledge graph system to production (not just prototyping) — schema design, query performance, tenant/data isolation - Using Neo4J and Python

• Strong background in semantic modeling: ontologies, taxonomies, business glossaries, and their relationship to graph and canonical data models.

• Hands-on experience with semantic web standards and technologies including OWL/OWL2, RDF, RDFS, SPARQL, ontology lifecycle management, taxonomy alignment, and semantic reasoning.

• Experience building data products and master-data pipelines (entity matching, golden-record consolidation, stewardship workflows).

• Experience designing and implementing hybrid retrieval architectures combining graph databases (e.g., Neo4j) and vector databases to support semantic search, RAG, and AI agent workloads.

• Experience with retrieval architectures combining graph and vector/embedding approaches.

• Experience building and maintaining labelled evaluation datasets and measuring extraction/model accuracy against a threshold.

• Proficiency with modern data/ML engineering practices: version control, CI/CD, automated testing, containerized deployment

• Experience with Microsoft Fabric and SharePoint-based document extraction pipelines.

• Fluent in ENG

• Open to travelling if needed (EU), this position is fully remote

• Collaborative mindset, ideally with a proven similar work experience