Data Scientist

GM Analytic Software; Bristol; On-site; GBP 55,000 - 75,000; Full time

Job summary

A specialized analytics firm in Bristol seeks a Data Scientist focused on graph databases and ontology management. This role involves designing production-grade graph solutions, collaborating cross-functionally, and integrating diverse data into accessible Knowledge Graphs. Ideal candidates will have at least 3 years in data science, strong Neo4j and Python skills, and experience deploying reliable systems. A master's degree in a related field is preferred. Join us to impact operations, HR, and more through effective data modeling.

Qualifications

• 3+ years of experience in Data Science or Data Engineering.
• MSc in Computer Science, Data Science, or a related engineering field.
• Practical delivery prioritized over theoretical knowledge.

Responsibilities

• Design and maintain scalable ontologies to unify mission data.
• Implement and optimize Neo4j schemas; write high-performance queries.
• Apply graph algorithms to derive actionable insights.
• Move solutions from research to production.
• Build ingestion pipelines for data into the Knowledge Graph.
• Translate operational requirements into robust data models.

Skills

Advanced Neo4j expertise

Data modeling

Strong Python proficiency

Graph ML experience

Experience with Docker

NLP for entity extraction

Geospatial data experience

Education

MSc in Computer Science or Data Science

Tools

Neo4j

Docker

FastAPI

Scikit-Learn

Job description

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.