Data Scientist, Fraud Risk Detection

Overview

Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms.

You will help investigate new fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers throughout the model development lifecycle, from problem definition and data assembly through feature engineering, experimentation, and deployment.

Your work will contribute to the techniques, analytical tools, and capabilities Experian's fraud analytics team has spent years refining. This is an individual contributor position and you will report into the Sr. Manager of Fraud Analytics

What you'll do

• Explore large datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals.
• Work with senior data scientists to translate fraud questions into testable hypotheses and analytical approaches.
• Help build machine learning models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using both technical and business metrics, such as precision, recall, fraud capture rate, false-positive rate, and customer friction.
• Develop and validate features using identity, transactional, behavioral, and other available data sources.
• Write clean, well-tested code, and work with engineering to bring models and features into production.
• Partner with the score monitoring team to help set up model and feature monitoring, and support research on related client questions.
• Help prepare analyses and communicate findings to both technical and nontechnical audiences.
• Apply Experian's standards for data privacy, model documentation, explainability, validation, and governance.

Qualifications & Requirements

• Bachelor's degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline - or equivalent experience
• 1+ years of professional experience in data science, machine learning, statistical modeling, or advanced analytics
• Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection.
• Proficiency in Python, with the ability to write clean, readable, and well-tested code.
• Familiarity with common data science and machine-learning tools such as pandas, NumPy, and scikit-learn.
• Coursework, internship, or project experience related to fraud detection, risk modeling, or another adversarial or classification-style problem.
• Familiarity with PySpark, cloud platforms such as AWS, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools.
• Exposure to financial services, fintech, payments, or another regulated or fraud-intensive industry, through coursework, internship, or prior work.
• #LI-Hybrid

Benefits/Perks

• Great compensation package and bonus plan
• Core benefits including medical, dental, vision, and matching 401K
• Flexible work environment, ability to work remote, hybrid or in-office
• Flexible time off including volunteer time off, vacation, sick and 12-paid holidays

• Our uniqueness is that we celebrate yours.
• Experian's people first, inclusive and purpose-driven culture is multi award-winning.
• We have won World's Best Workplaces™ 2025 (Fortune Global Top 25), and Great Place To Work™ in 26 countries to name a few.

• Experian's recruitment process is conducted only through authorised channels.
• Recruitment communications will only be sent from an @ experian.com email address.
• Experian will never ask candidates to make any payment as part of a recruitment process.

• Our compensation reflects the cost of labor across several U.S. geographic markets.
• Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education.
• You will be eligible for a variable pay opportunity and a comprehensive benefits package.