About this role
Are you ready to revolutionise the world with TEKEVER? 🚀🌍
At TEKEVER, we lead innovation in Europe as the European leader in unmanned technology, where cutting-edge advancements meet unparalleled innovation.
💻 Digital | 🛡️ Defence | 🔒 Security | 🛰️ Space
We operate across four strategic areas, combining artificial intelligence, systems engineering, data science, and aerospace technology to tackle global challenges — from protecting people and critical infrastructure to exploring space.
We offer a unique surveillance-as-a-service solution that delivers real-time intelligence, enhancing maritime safety and saving lives. Our products and services support strategic and operational decisions in the most demanding environments — whether at sea, on land, in space, or in cyberspace.
🌐 Become part of a dynamic, multidisciplinary, and mission-driven team that is transforming maritime surveillance and redefining global safety standards.
At TEKEVER, our mission is to provide limitless support through mission-oriented game-changers, delivering the right information at the right time to empower critical decision-making.
If you're passionate about technology and eager to shape the future — TEKEVER is the place for you. 👇🏻🎯
Mission:
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.
What will be your 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.
Profile and requirements:
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 We’re 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.
What we have to offer you:
An excellent work environment and an opportunity to make a difference;
Salary Compatible with the level of proven experience.
Do you want to know more about us ?
Visit our LinkedIn page at https://www.linkedin.com/company/tekever/