Data Scientist - Fraud

Equifax, Inc.; City Of London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

4 days in-office collaboration (Mon-TH

Friday flexibility

Job summary

Equifax UK Data & Analytics is seeking a data scientist to develop market leading fraud models and scores. You will use consumer and commercial bureau data, apply ML/AI techniques, and leverage cloud platforms to deliver predictive solutions.

You will collaborate with product, risk, and data teams to refine models and ensure robust deployment, documenting processes for governance and review.

Qualifications

• Experience using credit bureau data in statistical fraud models.
• Extensive experience developing statistical models and scores in fraud (consumer and commercial).
• Strong numerical ability with a relevant degree 2:1 or above.
• Proficient in Python, SAS, BigQuery, Jupyter Notebook, SQL on a daily basis.
• Experience developing regression models and scores (e.g., logistic).
• Familiarity with large/complex datasets and data extraction from multiple platforms.

Responsibilities

• Develop market leading statistical fraud models and scores using diverse data sources.
• Apply traditional and AI/ML techniques (logistic regression, gradient boosting, random forests).
• Utilise cloud based technologies and analytics tooling for test-and-learn approaches.
• Create new features to improve predictive power of models.
• Collaborate with stakeholders to deliver new scores and products.
• Document development processes for peer review and risk management.

Skills

Python

SAS

SQL

Jupyter Notebook

BigQuery

ML/AI techniques

Education

Numerate degree 2:1 or above

Tools

GCP

Vertex AI

Job description

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