Expert Data Scientist, Fraud Risk Detection

Experian

23h • Remote

About The Position

Experian’s Fraud Analytics & Commercialization team is seeking a Data Scientist to build fraud-detection models and features that identify high-risk activity while minimizing friction for legitimate customers. The role involves investigating fraud patterns and behavioral signals, developing and evaluating machine-learning models, and working with senior data scientists and engineers throughout problem definition, feature engineering, experimentation, and deployment. The position emphasizes collaboration, curiosity, and translating theoretical principles into production-ready solutions.

This remote role reports to the Sr. Manager of Fraud Analytics. The scientist will contribute to research and engineering that turn analytical findings into tools and systems for commercialization, and will follow standards for privacy, documentation, explainability, validation, and governance.

Requirements

• At least 5 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, including modular and tested code for large datasets and distributed or cloud data systems.
• Experience with 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 addressing fraud-modeling challenges, including class imbalance, delayed or incomplete labels, changing attack patterns, and model drift.
• Experience moving models beyond experimentation into production, directly or in close partnership with engineering teams.

Responsibilities

• Investigate large datasets through exploratory analysis and fraud-label development to identify fraud patterns, attack methods, and behavioral signals.
• Translate ambiguous fraud and risk problems into hypotheses, analytical plans, model requirements, and measurable success criteria.
• Develop machine-learning models for account-opening fraud, account takeover, and identity risk detection.
• Evaluate models using ROC/AUC/KS/Gini, precision and 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 on bringing 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 of model behavior, tradeoffs, risks, and recommendations.
• Follow standards for data privacy, model documentation, explainability, validation, and governance.

Benefits

• Great compensation package and bonus plan.
• Medical, dental, and vision benefits.
• Matching 401K.
• Flexible work environment, with remote, hybrid, or in-office options.
• Flexible time off, including volunteer time off, vacation, sick time, and 12 paid holidays.
• Variable pay opportunity and a comprehensive benefits package.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior