Finance
Business Support
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Data Scientist - Transaction Banking Data Domain
27-08-2026
5.304 - 7.577
Senior
Amsterdam
As a Data Scientist - Transaction Banking Data Domain at the AA&AI CoE, you develop ML/GenAI solutions, translate business challenges into insights, lead end-to-end analytics projects, explore large datasets, advise on KPIs/reporting, and drive automation with stakeholders.
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Data Scientist - Transaction Banking Data Domain
Role purpose: Apply advanced analytics and machine learning to transaction banking data to improve decision-making, risk controls, operational efficiency, and client outcomes across products such as payments, cash management, and trade finance. Key responsibilities - Develop, validate, and deploy predictive and descriptive models (e.g., forecasting, anomaly detection, segmentation, propensity, credit/fraud signals). - Translate business problems into data science solutions with clear success metrics, experimentation plans, and measurable impact. - Perform data exploration, feature engineering, and model evaluation; document assumptions, limitations, and performance. - Build scalable data pipelines and analytical datasets in partnership with data engineering; ensure data quality and lineage. - Communicate insights and recommendations to stakeholders using clear narratives and visualizations. - Support model governance, monitoring, and continuous improvement, including drift detection and retraining. Required skills and qualifications - Data science: Python or R, statistical modeling, machine learning, time-series analysis, and experimental design. - Data: SQL, data wrangling, feature engineering, and working with large-scale datasets. - Engineering & deployment: reproducible workflows, version control, and experience operationalizing models (APIs/batch scoring). - Domain: familiarity with transaction banking, payments, AML/fraud, liquidi...