Description

The Role : Data Scientist : Graph database and ontology specialist

We are seeking a Data Scientist with deep expertise in Knowledge Graphs and Ontologies and the ability to work across domains. You will design and deploy production-grade graph solutions that model relationships not only between UAVs, missions, and sensors, but across company processes end-to-end: from operations and production to HR and delivery. Your work will provide a transversal view of how data and processes interconnect, powering insights and decision-making across the organization.

Key Responsibilities

• Ontology Design & Management: Design and maintain scalable ontologies to unify mission data, sensor outputs, flight logs, and operational parameters.
• Graph Engineering (Neo4j): Implement, optimize, and operate Neo4j schemas; write high-performance Cypher queries and ensure production scalability.
• Graph Data Science: Apply graph algorithms (e.g., centrality, pathfinding, community detection) and graph ML to derive actionable insights.
• Production Deployment: Move solutions from research to production (TRL > 6); integrate graph models into APIs and pipelines with reliability and latency constraints.
• Data Integration: Build ingestion pipelines for structured and unstructured data into the Knowledge Graph.
• Cross-Functional Collaboration: Translate operational and domain requirements into robust data and graph models.

Requirements

Technical Skills

• Graph Databases: Advanced Neo4j expertise, including architecture, drivers, administration, and Cypher.
• Ontology & Semantics: Strong experience with data modeling, ontologies, and semantic technologies (RDF, OWL, SPARQL).
• Programming: High proficiency in Python (pandas, networkx, py2neo, neo4j-driver).
• Graph ML: Experience with Neo4j GDS or frameworks such as PyTorch Geometric or DGL.
• Production Engineering: Hands-on experience with Docker, REST APIs (FastAPI/Flask), and CI/CD pipelines.

Core Data Science Profile

• 3+ years of experience in Data Science or Data Engineering.
• Experience with NLP for entity and relationship extraction is a plus.
• Strongly skilled in standard ML workflows (Scikit-Learn, XGBoost)
• Experience with geospatial data (GIS, GeoPandas) is valued.

Education

• MSc in Computer Science, Data Science, or a related engineering field (PhD welcome, but practical delivery is prioritized).

Profile Were Looking For

• Production Builder: You focus on deploying reliable systems, not just experiments.
• Versatile Specialist: Deep in graph technologies, comfortable across the full data stack when needed.
• Structured Thinker: You value strong data models, data quality, and long-term maintainability.