Senior Data Scientist, ML, Fraud Prevention

What you'll do

• Build and tune ML models for fraud and abuse detection using statistical and classical techniques.
• Develop robust evaluation frameworks, datasets, and metrics to measure model and mitigation effectiveness.
• Analyze false positives, negatives, model drift, and emerging fraud patterns to improve detection.
• Define ground truth, labeling approaches, and fraud taxonomies to support reliable model development.
• Design experiments to evaluate tradeoffs across precision, recall, customer impact, and fraud loss.
• Build dashboards that translate detection performance into measurable business impact.
• Pressure-test models and data for leakage, bias, and other sources of misleading results.

What we're looking for

• 8+ years in applied Data Science / ML, with experience building and evaluating production ML models.
• Strong foundation in statistical and classical ML, experimentation, model evaluation, and performance measurement.
• Strong hands-on Python and SQL skills working with large, complex datasets.
• Experience with model monitoring, drift, false-positive/false-negative analysis, and imperfect or delayed labels.
• Strong data visualization and storytelling skills to translate complex analysis into clear insights and recommendations.
• Bachelor's or equivalent experience in Statistics, Mathematics, Computer Science, or related field; advanced degree a plus.
• Experience in fraud, abuse, risk, identity, trust & safety, or other adversarial domains (preferred).
• Experience with anomaly detection, clustering, behavioral modeling, or prevalence estimation (preferred).