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Transform Financial Crime Detection with Data – Apply your analytics experience to strengthen resilience and safeguard Barclays. In your new role as Analytics Assistant Vice President - US Quantexa, you will play a pivotal role in conducting detailed model analysis, tuning, optimization, and analytics across Transaction Monitoring systems deployed in the US and globally. You’ll collaborate with stakeholders including Control Owners, Risk, Compliance, Technology, Operations, and Model Validation teams, communicating clearly on model risk, performance, and lifecycle data. In your role, you will ensure TM solutions are rigorously built and embedded into routine processes, enabling early identification of money laundering and financial crime risks through advanced analytics. Alongside business-as-usual responsibilities, you’ll also contribute to transformation projects, helping design and deliver strategic detection solutions that strengthen Barclays’ financial crime prevention framework.
Job Responsibility:
Acquisition and collection of data from various sources, including internal databases, external datasets, and real-time feeds.
Performing data cleaning and pre-processing tasks to ensure data quality and suitability for model development.
Design and implementation of data management strategies for model maintenance and future development.
Designing, development, and implementation of statistical and machine learning models for various applications, including credit risk assessment, fraud detection, customer segmentation, and marketing optimisation.
Monitoring model performance in real-time and identify any potential issues or biases.
Leading training, mentoring, and knowledge-sharing sessions to uplift ML capability across the organisation.
Evaluating and piloting emerging technologies, tools, and frameworks to ensure the organisation remains at the forefront of AI innovation.
Requirements:
Advance coding skills like Python, PySpark, Scala or SQL
Analytics background with good understanding of databases, modelling, mathematics, and machine learning
Solving multi-faceted financial crime problems with logical analytics and ability to present them clearly with visualization MI tools, charts, processes, and business friendly language
Nice to have:
Financial crime experience especially in the area of Transaction Monitoring (TM)
Tuning and optimization, as well as model risk validation with understanding on various processes and responses to responses to complexities raised within TM models.