SUMMARY:
Design, deploy and optimise advanced analytics, machine-learning and graph-based solutions
POSITION INFO:
Senior Graph Data Scientist \/ TigerGraph Platform Engineer Role purpose To lead the design, engineering, deployment and operation of enterprise-scale graph data platforms and graph-based analytics solutions across the bank. The role combines deep expertise in TigerGraph, graph analytics, graph machine learning and knowledge graphs with hands-on ownership of AKS Kubernetes-based infrastructure , cloud-native deployment, data ingestion, performance optimisation and operational resilience. The successful candidate will enable relationship-driven intelligence across priority use cases including fraud detection, financial crime, AML, customer intelligence, network risk management, AI and GenAI . Key responsibilities Graph platform architecture and engineering Architect, design, deploy and operate secure, scalable and highly available TigerGraph clusters on Azure Kubernetes Service (AKS). Build and manage distributed graph infrastructure, including containerisation, orchestration, autoscaling, workload isolation, cluster management, monitoring and fault tolerance. Configure and optimise networking, storage, compute, identity, access control, secrets management and security controls for graph workloads in an enterprise environment. Ensure high availability, platform resilience, performance, capacity management and cost efficiency across TigerGraph and Kubernetes environments. Evaluate emerging graph, Kubernetes and cloud technologies to inform platform evolution and roadmap decisions. Graph data modelling and analytics Lead the design of advanced graph data models that represent complex relationships across customers, accounts, transactions, devices, merchants, organisations and other enterprise entities. Develop high-performing GSQL queries, graph algorithms and analytical engines to uncover hidden relationships, suspicious networks, behavioural patterns and business insights. Apply graph techniques including community detection, link prediction, path analysis, centrality, similarity analysis, entity resolution and network-risk scoring. Optimise graph query performance, workload throughput and resource utilisation across large-scale distributed graph environments. Design reusable graph-derived features to enhance downstream machine-learning models, decisioning systems and risk-scoring capabilities. Graph machine learning, AI and knowledge graphs Develop and operationalise graph-based machine-learning solutions, including graph neural networks and relationship-aware predictive models. Build and manage enterprise knowledge graphs that support advanced analytics, semantic intelligence, GenAI and retrieval-augmented generation use cases. Enable graph-enhanced AI solutions by connecting structured and unstructured enterprise information through relationship-centric data models. Partner with data scientists, AI engineers and business teams to translate graph insights into measurable business outcomes. Monitor and improve model accuracy, feature effectiveness, model performance and operational outcomes over time. Data integration and operationalisation Design and implement secure, scalable data-ingestion pipelines into TigerGraph from enterprise platforms such as Azure Data Lake Storage, Databricks, APIs, transactional systems and streaming data sources. Support both batch and real-time graph data ingestion, transformation and enrichment processes. Ensure graph solutions integrate effectively with enterprise data platforms, APIs, data products, risk systems and decisioning engines. Develop CI\/CD pipelines for graph applications, infrastructure and GSQL assets using Kubernetes-native and DevOps tooling. Establish monitoring, alerting, logging, observability and incident-management practices for graph platforms and graph-based services. Financial crime and enterprise use cases Deliver graph analytics solutions for fraud detection, financial crime, AML, suspicious-network identification, customer intelligence and network-risk management. Translate highly connected and complex financial-services data into practical, explainable and actionable business solutions. Support risk, fraud, compliance, customer and AI teams in identifying, prioritising and delivering high-value graph use cases. Ensure solutions meet enterprise requirements for security, governance, privacy, auditability and resilience. Leadership and stakeholder engagement Provide technical leadership and thought leadership on graph analytics, graph ML, TigerGraph, Kubernetes and graph-driven AI strategy. Mentor engineers and data scientists on graph data modelling, GSQL development, graph algorithms, Kubernetes operations and graph-based ML techniques. Communicate complex graph, infrastructure and AI concepts clearly to both technical and business stakeholders. Champion experimentation, innovation, reusable engineering standards and best prac
