Data Scientist - Fraud Risk

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.

What You’ll Do

• 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

We offer a hybrid work setting, comprehensive compensation and healthcare packages, attractive paid time off, and organizational growth potential through our online learning platform with guided career tracks.

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:

IND-Pune-Equifax Analytics-PEC

Function:

Function - Data and Analytics

Schedule:

Full time