Senior Data Scientist

Senior Data Scientist - Graph Analytics, AI and Financial Crime

Role purpose

To design, develop, deploy and optimise advanced analytics, machine-learning and graph-based solutions that generate actionable insight from complex, interconnected enterprise data.

The Senior Data Scientist will support high-impact banking use cases across fraud detection, financial crime, customer intelligence and AI/GenAI enablement. The role will be accountable for end-to-end model development, graph analytics, feature engineering, performance optimisation and production deployment within the bank's enterprise data and cloud environments.

Key responsibilities

Advanced analytics and machine learning

• Design, build and deploy predictive, classification, anomaly-detection and clustering models for complex business and risk use cases.
• Apply machine-learning techniques to identify fraud patterns, suspicious behaviour, financial-crime risk indicators and customer relationship insights.
• Develop robust model features from structured, semi-structured and interconnected data sources.
• Perform exploratory data analysis, data profiling, hypothesis testing and model validation.
• Evaluate, tune and optimise models for accuracy, scalability, interpretability and operational performance.
• Establish appropriate model-monitoring approaches, including drift detection, performance tracking and retraining considerations.

Graph analytics and knowledge graphs

• Design and develop graph-based data models representing relationships between customers, accounts, transactions, devices, merchants, organisations and related entities.
• Apply graph analytics techniques to identify hidden relationships, communities, network anomalies, suspicious transaction patterns and connected-risk indicators.
• Use graph algorithms such as centrality, similarity, path analysis, community detection, link prediction and entity resolution where relevant.
• Create graph-derived features for use in downstream machine-learning and risk models.
• Build and maintain knowledge-graph capabilities that enable AI and GenAI use cases, including improved retrieval, context enrichment, entity relationships and semantic understanding.
• Collaborate with data engineering and architecture teams to ensure graph data is scalable, governed and production-ready.

AI and GenAI enablement

• Support the development of AI and GenAI solutions through graph-enhanced data structures, semantic models and knowledge graphs.
• Contribute to retrieval-augmented generation, context enrichment and relationship-aware AI use cases where required.
• Partner with AI engineers, data engineers and solution architects to operationalise AI capabilities securely within the enterprise environment.
• Ensure AI and data-science solutions are aligned with responsible AI, data governance, privacy and model-risk requirements.

Data, platform and deployment accountability

• Work with data engineers to source, prepare and integrate data from enterprise platforms, transactional systems, APIs and external data sources.
• Develop reusable Python code, feature pipelines, model components and analytical assets.
• Package and deploy models using cloud-native, containerised or MLOps-aligned delivery practices.
• Contribute to model operationalisation, CI/CD pipelines, version control, documentation and production support.
• Optimise solutions for performance, scalability, reliability and maintainability.
• Ensure data quality, lineage, reproducibility and auditability across analytical and model-development processes.