Data Scientist, Fraud Risk Detection

Experian is hiring a Data Scientist, Fraud Risk Detection to investigate fraud patterns, develop features, and build and evaluate machine learning models. The role supports the model development lifecycle and contributes to fraud detection solutions across account opening, account takeover, and identity risk.

Responsibilities

• 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

Skills

• 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

Benefits

• Bonus plan
• Medical, dental, and vision benefits
• Matching 401K
• Flexible work environment, with the ability to work remote, hybrid, or in-office
• Flexible time off, including volunteer time off, vacation, and sick time
• 12 paid holidays
• 15 days of flexible time off

Company Overview

Company H1B Sponsorship