AVP - Data Scientist; Quantitative Analytics

Shape the Future of Financial Crime Prevention – Use your analytical strengths to build solutions that protect customers and strengthen our controls

In your role as AVP - Data Scientist (Quantitative Analytics) you will help us design and deliver machine learning solutions that enhance our ability to detect financial crime, prevent fraud, and safeguard our customers. Working within an established model development team and in close partnership with business stakeholders and engineers, you will focus on developing robust, intuitive machine learning models supported by scalable, production‑ready code and comprehensive monitoring and controls.

You will contribute across the full model lifecycle—from initial concept and data exploration through to supporting deployment—while maintaining the rigorous documentation and governance standards expected in a regulated environment. This role is well suited to professionals with validated experience in model development who want to apply advanced analytical techniques to real‑world fraud and financial crime complexities and make a meaningful impact with us.

• Direct experience in designing, developing, and deploying machine learning or statistical models within financial services or similarly regulated industries.
• Working experience coding in Python and experience with machine learning and distributed data frameworks (e.g. scikit‑learn, PyTorch, Spark).
• Confirmed experience in areas such as Fraud detection, Credit Risk, and Anti‑Money Laundering (or similar) in consumer banking.

• Responsibility for model lifecycle processes, from inception through development, deployment, and ongoing maintenance.
• Experience with cloud platforms (AWS, Azure, or GCP) or ML‑focused cloud‑based services (e.g. Databricks) for advanced data analytics and/or machine learning.
• Practical experience applying Dev Ops/MLOps fundamentals—version control (Git), unit testing, CI/CD pipelines, modular code design—and experience operationalizing models in collaboration with technology teams.
• An understanding of model risk management, governance, controls, and documentation within the financial services' regulatory environment.

This role is located in Wilmington, DE.

To 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, such as data, development environments and tools.
• Development of high performing, comprehensively documented analytics and modelling solutions, demonstrating their efficacy to business users and independent validation teams.
• Implementation of analytics and models in accurate, stable, well‑tested software and work with technology to ope rationalise 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.

• To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/business divisions.
• Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes.
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and…