[Hiring] Freelance Data Scientist with Ontology and Graph expertise @adesso Belgium

Role Description

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.

Qualifications

• 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.

Requirements

• Fluent in English.
• Open to travelling if needed (EU), this position is fully remote.
• Collaborative mindset, ideally with a proven similar work experience.

Benefits

• Work in an international group with strong local expertise in Belgium.
• Collaborate with cross-functional teams to deliver trusted, compliant, reusable data products.
• Flexible freelance setup with the support of a growing adesso Belgium community.