Expert Data Scientist, Fraud Risk Detection

Job Description:

• Investigate large datasets through exploratory analysis and fraud label development
• 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, account takeover, and identity risk fraud detection
• Evaluate models using 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
• 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 covering model behavior, tradeoffs, risks, and recommendations
• Follow standards for data privacy, model documentation, explainability, validation, and governance
• Report to the Senior Manager of Fraud Analytics
• Work with senior data scientists and engineers from problem definition through feature engineering, experimentation, and deployment

Requirements:

• 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
• Experience writing modular and tested code for large datasets and distributed or cloud data systems
• Experience with pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies
• Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and model calibration
• Experience with class imbalance, delayed or incomplete labels, changing attack patterns, and model drift
• Experience moving models beyond experimentation into production
• Compliance with applicable work authorization and employment requirements in the United States

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
• Comprehensive benefits package
• Variable pay opportunity
• Inclusive and purpose-driven culture
• Accommodation support for disabilities or special needs