Data Scientist, Fraud Risk

Focused on enhancing fraud detection, the full-time remote Data Scientist, Fraud Risk will manage onboarding fraud decisioning, build predictive models, and collaborate with cross-functional teams to combat identity theft and application abuse. Key responsibilities Own and improve onboarding fraud decisioning across the application journey, including identity verification and fraud models Build, validate, deploy, and monitor models to detect various forms of fraud using diverse data signals Design and analyze A/B tests and other experimental strategies to balance fraud losses against approval rates and verification friction Required qualifications 5 to 8+ years of experience in data science, risk analytics, or a related quantitative field, preferably in a high-growth startup or fintech environment Strong proficiency in Python and SQL for model building and data transformation Experience with predictive modeling for fraud, identity, KYC, or related adversarial classification problems Deep understanding of statistical inference, experiment design, and model validation techniques Ability to evaluate decision systems using metrics such as fraud capture and operational workload