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Senior Graph Data Scientist
80k - 100k + Permanent Benefits + Bonus
UK Wide - Flexible working
This position requires candidates to undergo SC Clearance, so you must be eligible (British/ILR)
A leading IT consultancy is looking to recruit a Senior Data Scientist specialising in Graph Technologies and Graph Data Science, alongside skills in machine learning, GenAI, data science and software engineering. You will work closely with key end clients to identify challenges, define impactful graph-based solutions, and clearly communicate how these solutions meet their goals.
Key Skills & Experience:
- Graph Data Science: proficiency in graph data analytics, e.g. depth-first/breadth-first search, community detection, graph embeddings and graph neural networks.
- Graph ontologies/data models: experience in ontology and knowledge graph design is strongly desired. An understanding of the distinction between Labelled Property Graphs and Resource Description Framework approaches would enhance your application.
- Graph Databases: demonstrable experience in using Neo4j, ArangoDB, Apache Jena or other graph databases/technology to derive business insights.
- Data Science Capabilities: working understanding of a range of AI techniques (e.g. supervised and un-supervised machine learning techniques, GenAI, deep learning, statistical analysis, time series, geospatial, NLP, sentiment analysis, pattern detection, etc.).
- Communication Skills: strong ability to present complex technical work to clients in a clear, concise manner, manage stakeholder relationships, and inspire team members.
- Cloud Platforms: demonstratable experience in building and deploying solutions to cloud platforms like AWS, Azure, or Google Cloud, using provisioning tools like Terraform.
- Technology Deployment: experience deploying solutions using Docker, Kubernetes, CI/CD platforms (e.g., Jenkins, ArgoCD), and GitHub for secure and robust implementations.
- Team Leadership: (for more senior applicants) a strong track record of leading technical teams, managing workloads, running Agile processes, and mitigating project risks.
