Data Scientist - Fraud Risk

• Develop market leading statistical scores and models for the Equifax UK business in relation to Fraud, Identity and Financial Crime
• Utilise and blend all available data sources to ensure all fraud related models are highly predictive and market leading - consumer bureau, commercial bureau, postcode insights, open source data & transactional insights
• Apply traditional model development techniques and approaches (e.g. linear & logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc. to create high performing fraud scores and models
• 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 fraud focused characteristics/attributes to improve and maximize the predictive power of the model(s) as well as enhancing and creating other analytical fraud products and services
• Investigate and apply appropriate segmentation and sub-populations to maximise overall fraud 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)

What experience you need

• 5+ years Extensive experience developing statistical fraud models and scores across the lending cycle e.g. application scores/models, customer management, debt recovery, etc.
• Highly numerate with a relevant degree e.g. Mathematics, Statistics, Computer Science
• Use of relevant analytical tooling on a day-to-day basis e.g. Python, BigQuery, Jupyter Notebook, SQL, etc.
• Previous experience of developing fraud focused regression models and scores - linear and logistic
• Day to day exposure to large/complex datasets, analytical tools, spreadsheets, analytical platforms, etc.
• Experience using credit bureau data in statistical models - raw and summarised data
• Able to extract data from multiple platforms and create master datasets towards the development of statistical models
• A team player, effective communicator with the ability to work unsupervised

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 alternative data such as open banking transactional data in statistical models
• Experience of working in a product development environment
• 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