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.

·       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

·       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.

·       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