Freelance Data Scientist with Ontology and Graph expertise

Responsibilities

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

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