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 graphswith 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