Senior Graph Data Scientist Position

3 weeks ago

London, Greater London, United Kingdom Amber Labs Full time
About the Role

We are seeking a highly skilled Graph Data Scientist to join our End-User Computing (EUC) team within the Nuclear Sector at Amber Labs. As a key member of our team, you will play a crucial role in designing, analysing, and leveraging graph data models to support complex nuclear operations, research, and safety processes.

Key Responsibilities
  • Develop and implement graph-based data models for analysing complex relationships within nuclear engineering, safety, and operations systems using tools like Neo4j, GraphX, and Gremlin.
  • Conduct exploratory data analysis (EDA) and use graph algorithms (e.g., pathfinding, community detection, centrality) to gain insights from complex datasets.
  • Develop and deploy machine learning models on graph structures for predictive analytics, anomaly detection, and risk analysis, supporting nuclear operations and safety requirements.
  • Perform statistical analysis and data mining on large-scale, multi-dimensional datasets to identify key trends and patterns.
  • Collaborate with nuclear engineers, data scientists, and IT teams to understand data sources and apply graph analytics to enhance system performance, reliability, and safety.
  • Implement and optimize graph query performance using languages like Cypher and tools like Neo4j to analyse real-time data streams.
  • Work with cloud-based platforms (AWS, Azure, GCP) and on-premise systems to build, train, and deploy graph-based models in production environments.
  • Ensure compliance with data governance and security protocols related to the nuclear sector, working within stringent regulatory frameworks to ensure the safety and integrity of data systems.
  • Collaborate on the development of data pipelines and ETL processes to integrate diverse sources of data into graph databases, ensuring scalability and reliability.
  • Engage with DevOps teams to automate the deployment of graph-based data solutions and manage model lifecycle processes (ML Ops).
  • Produce technical reports, dashboards, and visualizations to present complex graph-based findings to non-technical stakeholders and decision-makers.

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