Expert Data Scientist, Fraud Risk Detection

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.

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.