Senior Data Scientist

SUMMARY:
Design, deploy and optimise advanced analytics, machine-learning and graph-based solutions

POSITION INFO:
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.