[Hiring] Senior Data Modeler, Fraud Risk Detection @Experian

Role Description

Experian's Fraud Analytics & Commercialization operates across four main functions, including:

• Client engagement analytics
• Scalable and custom analytics for financial institutions
• Fraud analytics consulting
• Solution integrity and enablement for production-ready platforms

We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include:

• Strategic thinking
• Eagerness to collaborate
• Empathy

You will:

• Investigate the latest fraud patterns
• Build features
• Train and evaluate machine learning models
• Work with senior data scientists and engineers from problem definition through feature engineering, experimentation, and deployment
• Translate theoretical principles into production-ready solutions

This is a remote role and you will report into the Sr. Manager of Fraud Analytics.

What you'll do

• Explore complex datasets to identify fraud patterns, attack methods, and behavioral signals
• Work with senior data scientists to translate fraud questions into testable hypotheses
• 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
• 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

• 1+ years of experience in data science, machine learning, statistical modeling, or a related quantitative field
• Bachelor's or advanced degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline
• 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
• Investigative mindset and the ability to move from unusual data patterns to testable hypotheses
• Familiarity with PySpark, cloud platforms such as Amazon Web Services, 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

• 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
• Explore all our exciting benefits here: Experian Benefits
• Inclusive and purpose-driven culture
• Recognition as a World's Best Workplaces™ 2025 (Fortune Global Top 25) and Great Place To Work™ in 26 countries