Date live:04/09/2025
Business Area:Fraud Analytics Strategy Technology
Area of Expertise:Data & Analytics
Contract:Permanent
Reference Code:JR-0000048158
As a Fraud Data Scientist at Barclays, you will be responsible for the development and enhancement of fraud detection systems. Applying advanced analytical methods and data-driven approaches, you’ll improve our ability to detect and prevent fraud across a variety of banking products and services. You’ll work closely with other experts in the field, helping us stay one step ahead in addressing fraud risks.

To be successful as a Fraud Data Scientist, you should have experience with:

• A Degree in Mathematics, Statistics, Computer Science, or a related field (or equivalent work experience).
• Experience in fraud detection, scam prevention, or cybersecurity, ideally in a financial services or banking environment.
• Proficiency in data analysis, with hands-on experience using tools and languages such as Python, R, SQL and machine learning frameworks.

You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills.
This role will be located at our Northampton office.

ACCOUNTABILITIES

• Identification, collection, extraction of data from various sources, including internal and external sources.
• Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
• Development and maintenance of efficient data pipelines for automated data acquisition and processing.
• Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
• Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
• Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.

To use innovative data analytics and machine learning techniques to extract valuable insights from the bank’s data reserves, leveraging these insights to inform strategic decision-making, improve operational efficiency, and drive innovation across the organisation.