[Hiring] Expert Data Scientist, Fraud Risk Detection @Experian

Role Description

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

• 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 an eagerness to collaborate and empathy.

You will:

• Investigate large datasets, including exploratory analysis and fraud label development, to identify latest fraud patterns, attack methods, and behavioral signals.
• Translate ambiguous fraud and risk problems into clear hypotheses, analytical plans, model requirements, and measurable success criteria.
• Develop machine learning models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using metrics like ROC/AUC/KS/Gini, precision/recall, fraud capture rate, false-positive rate, customer friction, and fraud losses prevented.
• Develop and validate predictive features using identity, transactional, consumer credit history, device, behavioral, temporal, velocity, network, and third-party data.
• Write clean, efficient, well-tested Python and PySpark code, and collaborate with teams to bring models and features into batch, retro, or real-time decisioning environments.
• Monitor feature quality, model performance, population changes, and fraud-pattern drift.
• Design and present analyses for model behavior, tradeoffs, risks, and recommendations.
• Follow appropriate standards for data privacy, model documentation, explainability, validation, and governance.

Qualifications

• At least 3 years of experience in data science, machine learning, statistical modeling, or a related quantitative field
• Bachelor's or advanced degree in computer science, statistics, engineering, data science, or another quantitative discipline
• Direct experience developing fraud-detection, identity-risk, credit-risk, financial-crime, or other adversarial risk models
• Demonstrated experience creating meaningful fraud features
• Proficiency in Python and PySpark, with experience writing modular and tested code for large datasets and distributed or cloud data systems
• Experience using common data science and machine-learning tools such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies
• Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and model calibration
• Experience navigating challenges common to fraud modeling, including class imbalance, delayed or incomplete labels, changing attack patterns, and model drift
• Experience moving models beyond experimentation and into production, either directly or in close partnership with engineering teams

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

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 is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities.