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In your role as Data Scientist (Machine Learning), you will design and build machine learning solutions that strengthen the bank’s ability to detect financial crime, prevent fraud, and safeguard customers. Working within an established model development team and alongside business stakeholders and engineers, your role focuses on the development of robust and intuitive machine learning model solutions, delivered via scalable, production-grade code, accompanied by comprehensive monitoring and controls. You will support the full model lifecycle—from inception and data exploration through supporting model deployment—while adhering to rigorous documentation and governance practices as required in a regulated environment. This position is designed for early-career data scientists with a good academic foundation who are passionate about applying machine learning to real-world fraud and financial crime challenges.
Job Responsibility:
Design and build machine learning solutions that strengthen the bank’s ability to detect financial crime, prevent fraud, and safeguard customers
Support the full model lifecycle—from inception and data exploration through supporting model deployment
Design, develop, implement, and support mathematical, statistical, and machine learning models and analytics used in business decision-making
Design analytics and modelling solutions to complex business problems using domain expertise
Collaboration with technology to specify any dependencies required for analytical solutions
Development of high performing, comprehensively documented analytics and modelling solutions
Implementation of analytics and models in accurate, stable, well-tested software and work with technology to operationalise them
Provision of ongoing support for the continued effectiveness of analytics and modelling solutions to users
Demonstrate conformance to all Barclays Enterprise Risk Management Policies, particularly Model Risk Policy
Ensure all development activities are undertaken within the defined control environment
Requirements:
An academic background in quantitative or computational discipline (mathematics, statistics, computer science, engineering, or related fields) with exposure to machine learning concepts
Practical coding ability in Python and familiarity with machine learning libraries and distributed data frameworks
Considerable understanding of core principles in machine learning, statistical modelling, data analysis, and algorithmic thinking
Nice to have:
Exposure to aspects of model development (data preparation, development, deployment, monitoring)
Familiarity with cloud platforms (AWS, Azure, or GCP) or ML-focused services (e.g. Databricks)
Keen interest in DevOps/MLOps fundamentals—version control (Git), unit testing, CI/CD pipelines, modular code design
Awareness of model risk management, governance, and controls within the financial services’ regulatory environment