TEKsystems require a Financial Crime Data Scientist for a contract role with a banking client in London.
Supporting the client's AML Programme the Data Scientist will contribute to the development of their AML transaction monitoring system.
Other elements include customer screening, payment screening and fraud monitoring.
• Advising the financial crime SMEs on the most appropriate mathematical and statistical approaches to identify customer segments and monitoring thresholds.
• Performing exploratory data analysis to develop the understanding of the client's customers’ transactional behaviour.
• Application of supervised and unsupervised machine learning algorithms to our customers’ transactional datasets to establish what is ‘normal’ and what is ‘unusual’ behaviour.
• Application of clustering techniques to establish outliers in the dataset.
• Application of advanced techniques to transactional datasets to establish mathematically and statistically sound monitoring thresholds.
• Building a relationship with the bank-wide Technology Data Analytics function.
• MSc or PhD with excellent academic results in the field of mathematics, statistics, computer science or similar.
• Machine learning skills, i.e. classification, regression, clustering, unsupervised methods, text mining. An excellent understanding of random forests, isolation forests, gradient boosting, neural networks, logistic regression, SVM, KNN, K-Means, etc. is expected. Parametric and non-parametric statistics are essential.
• Programming languages, i.e. Python, Java etc.
• Database skills, i.e. SQL.
• Visualisation skills, i.e. using Qlikview, Tableau, Power BI, Business Objects, Cognos or similar.
• Banking sector financial crime prevention technology knowledge and experience.