Data Scientist - Fraud

Salary: £67,000 - 107,000 per year

Requirements:

• Relevant degree with a 2:1 or above, and strong numerical skills.
• Extensive experience developing statistical models and scores, particularly for consumer and commercial fraud.
• Experience using raw and summarised credit bureau data in statistical fraud models.
• Experience developing regression models and scores, such as logistic regression.
• Day-to-day experience using analytical tools such as Python, SAS, BigQuery, Jupyter Notebook, and SQL.
• Experience working with large or complex datasets, analytical tools, spreadsheets, and analytical platforms.
• Ability to extract data from multiple platforms and create master datasets for statistical model development.
• Experience across fraud types such as consumer fraud, commercial fraud, Anti-Money Laundering, and KYC.
• Ability to work unsupervised and collaborate effectively as a team player and communicator.
• Understanding of the regulatory landscape for consumer and commercial credit and its potential impact on clients.
• Helpful additional experience includes developing AI/ML fraud solutions, using G-Suite, GCP, Vertex AI, or LLMs, and working with alternative data such as open banking transactions.
• Helpful additional experience includes working in a credit reference agency or client environment, product development, fraud prevention tools, product testing procedures, and champion/challenger approaches.
• Creative and innovative approach, with a strong track record of delivery and willingness to go above and beyond.

Responsibilities:

• Develop statistical fraud models and pseudo-models for our UK business, including consumer and commercial fraud models, synthetic fraud, payment intent models, and loan-stacking indicators.
• Combine available data sources, including bureau, postcode, open-source, transactional, and partnership fraud data, to build predictive models.
• Apply traditional modelling techniques and AI/ML approaches, including logistic regression, gradient boosting, random forests, clustering, and transformers.
• Use cloud technologies and analytical tools to support test-and-learn champion/challenger approaches for new fraud scores and models.
• Create characteristics and attributes that improve model predictive power and enhance our analytical products and services.
• Investigate and apply appropriate segmentation and sub-populations to improve overall model and score performance.
• Liaise with internal stakeholders, including Product Managers, Model Risk Management, Pre-Sales, Consultancy, Compliance, Technology, and Data & Analytics, to deliver scores and products.
• Collaborate with data scientists and analytical teams across our global business to share best practices and learnings.
• Follow relevant model development policies and procedures, including those for sample design, good/bad definitions, exclusions, development, validation, and implementation.
• Produce documentation to support peer review and Model Risk Management processes.

Technologies:

• AI
• BigQuery
• Cloud
• GCP
• Support
• Jupyter
• Mobile
• Python
• SAS
• SQL
• Machine Learning

More:

We are Equifax, and our UK Data & Analytics team develops market-leading fraud scores, models, and analytical solutions using cloud-based technologies and modern techniques. We are a growing, diverse team creating the next generation of statistical models, machine-learning algorithms, and AI-based products and services. Our working schedule combines in-office collaboration Monday through Thursday with remote flexibility on Fridays. We offer benefits including a contributory pension, life cover, income protection, healthcare, enhanced maternity and sick pay, 26 days of holiday plus a birthday day off, the option to buy or sell holiday, and free credit checks. Additional flexible benefits include cycle-to-work and travel options, gym membership, dining and leisure discounts, financial and savings plans, and mobile discounts. We support personal development through a global online learning platform and inclusion, diversity, wellbeing, and employee engagement forums. This is a full-time role based in London, Leeds, or Nottingham.

last updated 40 week of 2026