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Equifax

Credit reporting data analytics and risk

Data Scientist - Fraud

Data ScienceConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • Experience using credit bureau data in statistical fraud models, including raw and summarised data.
  • Extensive experience developing statistical models and scores, particularly for consumer and commercial fraud.
  • A relevant degree at 2:1 or above.
  • Day-to-day use of relevant analytical tools such as Python, SAS, BigQuery, Jupyter Notebook, or SQL.
  • Previous experience developing regression models and scores, such as logistic regression.
  • Day-to-day exposure to large or complex datasets, analytical tools, spreadsheets, and analytical platforms.
  • Ability to extract data from multiple platforms and create master datasets for developing statistical models.
  • Experience across different fraud types, such as consumer fraud, commercial fraud, Anti-Money Laundering, and KYC.
  • Ability to work unsupervised.
  • Understanding of the regulatory landscape for consumer and commercial credit and its potential impact on clients.

Responsibilities

  • Develop statistical fraud models and pseudo-models for the UK business, including consumer and commercial fraud models, synthetic fraud, payment intent models, and loan-stacking indicators and models.
  • Use and blend available data sources to ensure fraud models are predictive, including consumer bureau data, postcode insights, open-source data, transactional insights, and fraud indicators from partnerships.
  • Apply traditional model-development methods such as logistic regression and AI/ML methods such as gradient boosting, random forests, and clustering.
  • Use cloud-based technologies and analytical tools to support test-and-learn champion/challenger approaches for new fraud scores and models.
  • Create new characteristics or attributes to improve model predictive power and enhance or create analytical products and services, using expert or AI approaches such as transformers.
  • Investigate and apply appropriate segmentation and sub-populations to maximise 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.
  • Work with Data Scientists and analytical teams across the global business to share best practices and learnings.
  • Follow model-development policies and procedures for deploying scores and models, including sample design, good/bad definitions, rationale for exclusions, model development, validation, and implementation.
  • Produce documentation alongside statistical model development to support peer review and Model Risk Management processes.

Desired Qualifications

  • Experience developing AI/ML-based fraud solutions and models.
  • Day-to-day use of G-Suite tools, GCP, and VertexAI.
  • Experience working at a credit reference agency or in a client environment such as banking, lending, insurance, the public sector, or utilities.
  • Use of large language models to improve efficiency in the model-building process.
  • Experience using alternative data, such as open banking transactional data, in statistical models.
  • Experience working in a product-development environment.
  • Experience with industry-related fraud-prevention tools such as Hunter, Kount, SEON, ThreatMetrix, LexisNexis, Minerva, CrossCore, TruValidate, or FICO Falcon.
  • Experience with standard product-testing procedures, including UAT, OAT, and regression testing.
  • Adoption of champion/challenger approaches and techniques.
  • A track record of delivering work effectively.

About the Company

Equifax

Credit reporting data analytics and risk

Equifax is a global data, analytics, and technology company that provides credit information and related services to businesses, governments, and consumers. It collects and analyzes large amounts of data to generate insights used for credit reporting, risk management, fraud detection, and identity verification. The company uses its Equifax Cloud to combine data with advanced analytics and machine learning, delivering credit reports, risk assessments, and global market insights to help clients make smarter decisions and improve customer experiences. Compared with competitors, Equifax emphasizes its large, differentiated data assets and integrated analytics platform to offer comprehensive, end-to-end solutions for credit and risk management. Its goal is to help clients assess credit risk, detect fraud, and explore opportunities more confidently in a changing financial landscape.

Company size
10,001+
Company stage
IPO
Headquarters
Atlanta, GA
Founded
1899

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