Freelance Data Scientist with Ontology and Graph expertise

adesso Belgium; United States; Remote; USD 110,000 - 180,000; Full time

Benefits offered by this job

Flexible freelance setup

Remote work with EU travel eligibility

International team support

Job summary

adesso Belgium is seeking a data scientist to design and operate a production-grade AI agent platform data foundations, including canonical models, a knowledge graph, and a semantic/taxonomy layer. You will own data products from ingestion to canonical form, with a multi-tenant, versioned architecture.

Responsibilities include building data products, mastering data governance, and implementing hybrid retrieval architectures that combine graph databases and embeddings to support AI workloads.

Qualifications

• Experience taking a knowledge graph system to production with schema design and tenant isolation.
• Strong background in semantic modeling and ontologies, taxonomies, glossaries.

Responsibilities

• Build data products and canonical modeling end to end (Supplier Master, Spend Cube, Contract Register).
• Stand up knowledge graph, ontology, and tenant separation; manage graph-plus-vector retrieval design.
• Develop master-data pipelines, matching rules, golden records, and governance workflows.
• Create extraction pipelines from sources like SharePoint with confidence scoring and human review.
• Implement connectors to onboard new data sources without core changes to graph/service.
• Establish data quality feedback loops to improve data products over time.
• Work in GitHub/Azure with PRs, tests, and CI/CD; version schemas and configs.

Skills

Knowledge graph production

Semantic modeling

Graph databases

Python

Data product development

Hybrid retrieval architectures

CI/CD

SharePoint data extraction

Tools

Neo4j

SPARQL

RDF/OWL

GitHub

Azure DevOps / CI

Job description

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

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

Requirements Preferred start date: 1xASAP and 1x January 2027 Duration: 12 months Rate: depending on your location Time Zone: CET

Your role

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.

Required Experience Hard skills:

• 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/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.

Soft skills:

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

Why join adesso Belgium ?

• 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.