Fraud Data Scientist

Required

• 4+ years as a data scientist or analyst working on fraud, risk, or abuse, ideally at a payments company, fintech, or marketplace
• You've used analysis to measurably improve production fraud models or rules: new features, better thresholds, lower loss, higher approvals
• Strong SQL and Python. Fluent with the stats and ML concepts behind fraud models (gradient-boosted trees, calibration, imbalanced data) even if you're not the one deploying them
• Solid grasp of the fraud problem space: card-not-present fraud, account takeover, card testing, friendly fraud, chargeback reason codes, and how fraudsters adapt
• Comfort with messy, imbalanced, delayed-label data and the judgment to evaluate model performance honestly on it
• Experience designing and analyzing experiments where a false positive and a false negative cost very different amounts
• A strong feature intuition: you can look at a fraud pattern and propose the signal that would have caught it
• Ability to explain model behavior and tradeoffs to merchants, risk ops, banks, and auditors in plain language
• Familiarity with graph or network analysis for detecting linked accounts and fraud rings is a plus
• Comfort working with AI-native tooling as a daily driver, or genuine excitement to start
• A bias toward action. We'd rather have a clear recommendation this week than a perfect analysis next quarter
• The base salary range for this role is $120,000 - $180,000 USD + equity. The actual base salary offered depends on a variety of factors, including but not limited to experience, education, skills, qualifications, and business needs.