Senior Data Scientist

· Lead the design and development of advanced graph data models, graph algorithms, and graph-based machine learning solutions to unlock complex relationship insights and enterprise value. · Translate highly connected and complex data into actionable business solutions using TigerGraph and graph analytics techniques within financial services contexts. · Architect, deploy, and operate scalable TigerGraph clusters on AKS Kubernetes, ensuring high availability, fault tolerance, and optimal resource utilisation. · Drive the operationalisation of graph-based analytics and machine learning use cases, ensuring production robustness, scalability, and alignment with business objectives. · Design, build, and manage distributed graph infrastructure on Kubernetes, including containerisation, orchestration, autoscaling, and cluster management. Implement secure and performant data ingestion pipelines into TigerGraph from enterprise data platforms (e.g. ADLS, Databricks), supporting batch and real-time processing. · Configure and manage networking, storage, and security for graph workloads on AKS, including integration with enterprise identity, access control, and secrets management. · Optimise graph query performance (GSQL), workload isolation, and system throughput across large-scale distributed environments. · Apply advanced graph techniques such as graph neural networks, link prediction, community detection, and path analysis to solve high-impact use cases. · Build and manage enterprise knowledge graphs, enabling advanced analytics, GenAI, and RAG capabilities grounded in relationship-centric data. · Enable feature engineering and reuse through graph-derived features, enhancing downstream machine learning models and decisioning systems. · Deliver high-impact graph analytics solutions across fraud detection, financial crime, customer intelligence, and network risk management. Requirements · Oversee end-to-end graph solution architecture, ensuring seamless integration with data platforms, APIs, and enterprise systems. · Develop CI/CD pipelines for graph applications and infrastructure using Kubernetes-native and DevOps tooling, enabling automated deployment and monitoring. · Provide thought leadership on graph and Kubernetes strategy, embedding scalable graph capabilities into enterprise AI platforms. · Mentor teams on graph modelling, GSQL development, Kubernetes operations, and graph-based ML techniques. · Continuously monitor and optimise system health, cluster performance, cost efficiency, and model accuracy in dynamic environments. · Evaluate emerging tools across graph, Kubernetes, and cloud ecosystems to inform platform evolution and roadmap development. · Communicate complex graph and infrastructure concepts clearly to business and technical stakeholders. · Champion experimentation and innovation in graph analytics and distributed systems engineering. · Support strategic initiatives, embedding graph platforms into enterprise digital and AI transformation programmes. Benefits TigerGraph certification, Kubernetes (CKA/CKAD), and cloud platform certifications (Azure preferred