AVP Data Scientist (Quantitative Analytics)

Barclays•New York, NY

26d•$100,000 - $160,000

About The Position

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. To be successful in your role as AVP - Data Scientist (Quantitative Analytics), you should have: 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. Other highly valued skills include: Responsibility for model lifecycle processes, from inception through development, deployment, and on-going 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 DevOps/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. You may be assessed on the key critical skills relevant for success in this role, such as risk and controls, change and transformation, business acumen, strategic thinking, digital and technology, as well as job-specific technical skills. This role is located in New York, NY and a secondary location of Wilmington, DE. Applies to New York location only Minimum Salary: $100,000.00 Maximum Salary: $160,000.00 The minimum and maximum salary/rate information above includes only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available. Purpose of the role To design, develop, implement, and support mathematical, statistical, and machine learning models and analytics used in business decision-making

Requirements

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 on-going maintenance.
An understanding of model risk management, governance, controls, and documentation within the financial services' regulatory environment.

Nice To Haves

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 DevOps/MLOps fundamentals—version control (Git), unit testing, CI/CD pipelines, modular code design—and experience operationalizing models in collaboration with technology teams.

Responsibilities

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 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.

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