Senior Data Scientist (Contract)

Job Purpose

Apply deep expertise in TigerGraph, graph analytics, and graph-based machine learning to architect and implement enterprise-scale graph platforms and advanced analytic engines on AKS Kubernetes, enabling high-performance, relationship-driven intelligence and providing expert guidance on graph-driven AI solutions.

Job Responsibilities

• 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.

• 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.

Essential Qualifications

• NQF Level

• BSc Computer Science, Engineering, Mathematics, Statistics, or related STEM field.

Preferred Qualification

• Master’s Degree in Computer Science, Engineering, Mathematics, Statistics, or related STEM field.

Preferred Certifications

• TigerGraph certification

• Kubernetes (CKA/CKAD)

• Cloud platform certifications (Azure preferred).

Type of Exposure

• Graph engineering and large-scale graph platform deployment

• AKS Kubernetes cluster design and operations

• Distributed systems and cloud-native architecture

• Financial crime and fraud analytics using graph techniq