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Data Scientist - Fraud

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Employment
Full-time — employment source
Schedule: Full time
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting
  • Develop statistical fraud models and scores for consumer, commercial and synthetic fraud

  • Blend credit bureau, postcode, open source and transactional data into fraud models

  • Apply logistic regression alongside gradient boosting, random forests and clustering

  • Run champion/challenger tests of new fraud scores on cloud tooling

  • Produce model documentation for peer review and Model Risk Management

From the employer’s posting
Develop market leading statistical fraud models and pseudo-models for the Equifax UK business
Utilise and blend all available data sources to ensure all fraud models are highly predictive and market leading
Apply traditional model development techniques and approaches (e.g. logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc.
Utilise the latest cloud based technologies and analytical tooling to support test-and-learn champion/challenger approaches towards new fraud scores and models
Produce suitable documentation alongside the statistical development of the model(s) to support peer review and Model Risk Management processes
Apply at Equifax

Posting record

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Status in our records
Live
First seen by us
Sep 25, 202640 days ago
Recorded sightings
105
Last seen by us
Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 40

Similar jobs usually come down between day 7 and day 55.

At Equifax, jobs usually come down by about day 23.

From 10,040 similar postings that came down, and 71 of Equifax’s. Coming down is not always a hire.

Equifax is hiring

17 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Data Scientist hiring is steady
  • Takes jobs down quickly
  • 12% of open jobs were reposted
Hiring record

See how this role fits your experience

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  • Python
  • SAS
  • SQL
  • BigQuery
Where the posting names them
- Highly numerate with a relevant degree 2:1 or above - Use of relevant analytical tooling on a day-to-day basis e.g. Python, SAS, BigQuery, Jupyter Notebook, SQL, etc. - Previous experience of developing regression models and scores - e.g. logistic

Your next step

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About the role

We file this job under Data Scientist, by the work the posting describes.

Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.

Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.

Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.

US government figures for data scientists

Median yearly pay, May 2025
$120,230
Expected change, 2025 to 2035
34.6% more jobsagainst 3.5% for all jobs

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.

Job description

View original posting ↗

Equifax is where you can power your possible. If you want to achieve your true potential, chart new paths, develop new skills, collaborate with bright minds,  and make a meaningful impact, we want to hear from you.

Come join the Equifax UK Data & Analytics  team and develop market leading fraud scores, models and analytical solutions using the latest cloud based technologies, techniques and tooling. Be part of a growing and diverse team tasked with creating the next generation of statistical models, machine learning algorithms and AI based products and services.


We believe great things happen when teams connect. Our schedule is built around 4 days of high-impact, in-office collaboration (Monday–Thursday), paired with Friday Flexibility to wrap up your week remotely. 


What You’ll Do


  • Develop market leading statistical fraud models and pseudo-models for the Equifax UK business e.g. consumer fraud models, commercial fraud models, synthetic fraud, payment intent models, and loan stacking indicators and models Utilise and blend all available data sources to ensure all fraud models are highly predictive and market leading - consumer bureau data, postcode insights, open source data,transactional insights & fraud indicators from partnerships
  • Apply traditional model development techniques and approaches (e.g. logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc.
  • Utilise the latest cloud based technologies and analytical tooling to support test-and-learn champion/challenger approaches towards new fraud scores and models
  • Consider the creation of new characteristics/attributes to improve and maximise the predictive power of the model(s) as well as enhancing and creating other analytical products and services. This can be done through expert approaches or through AI approaches (e.g. transformers)
  • Investigate and apply appropriate segmentation and sub-populations to maximise overall model & score performance
  • Liaise with internal stakeholders in a timely and effective manner towards the delivery of new scores and products e.g. Product Managers, Model Risk Management, Pre-Sales, Consultancy, Compliance, Technology, Data & Analytics etc.
  • Work closely with Data Scientists and analytical teams globally across the Equifax business to share best practice and learnings
  • Adhere to relevant model development policies and procedures towards a successful deployment of scores and models e.g. sample design, good/bad definitions, rationale for exclusions, model development, model validation, implementation, etc.
  • Produce suitable documentation alongside the statistical development of the model(s) to support peer review and Model Risk Management processes

What experience you need


  • Experience using credit bureau data in statistical fraud models - raw and summarised data
  • Extensive experience developing statistical models and scores particularly within fraud (consumer and commercial)
  • Highly numerate with a relevant degree 2:1 or above
  • Use of relevant analytical tooling on a day-to-day basis e.g. Python, SAS, BigQuery, Jupyter Notebook, SQL, etc.
  • Previous experience of developing regression models and scores - e.g. logistic
  • Day to day exposure to large/complex datasets, analytical tools, spreadsheets, analytical platforms, etc.
  • Able to extract data from multiple platforms and create master datasets towards the development of statistical models
  • Experience across different fraud types, e.g. consumer fraud, commercial fraud, Anti-Money Laundering, KYC.
  • A team player, effective communicator with the ability to work unsupervised
  • Understanding of the regulatory landscape with respect to consumer & commercial credit and the potential impact to our clients (i.e. addressing the "why" behind our decision making)

What could set you apart


  • Experience of developing AI/ML based fraud solutions and models
  • Day to day use of G-Suite tooling, GCP and VertexAI
  • Experience of working in a credit reference agency and/or client environment e.g. Banking, Lending, Insurance, Public Sector, Utilities, etc.
  • Use of LLMs to improve efficiencies in the model building process
  • Use of alternative data such as open banking transactional data in statistical models
  • Experience of working in a product development environment
  • Experience with industry related fraud prevention tools (e.g. Hunter, Kount, SEON, ThreatMetrix, Lexisnexis, Minerva, CrossCore, TruValidate, FICO Falcon, etc.)
  • Experience with standard product testing procedures (UAT, OAT, regression, etc.)
  • Adoption of champion/challenger approaches and techniques
  • Creative and innovative thinker
  • Strong track record of delivery
  • Team player with the ability to go above and beyond

The Perks of being a Equifax Employee?

We offer a wide range of company supported benefits including contributory pension, life cover, income protection, healthcare, enhanced maternity and sick pay, 26 days holiday and a day off for your birthday, with the ability to buy and sell and free credit checks. We also offer flexible benefits ranging from cycle to work, discounted travel options, gym membership, dining and leisure discounts, financial & savings plans, mobile discounts plus much more!

We also support personal development and have a range of learning options including our global online learning platform and allow you to bring your whole self to work supported by our Inclusion and Diversity, Wellbeing and Employee Engagement forums.

Are you ready to power your possible?  Apply today, and get started on a path toward an exciting new career at Equifax, where you can make a difference!
 

Primary Location:

GBR-London

GBR-Leeds-6-Wellington, GBR-Nottingham

Function:

Function - Data and Analytics

Schedule:

Full time
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