Data Scientist : Graph Database & Ontology Specialist in Bristol

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. 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 TEKEVER LinkedIn.