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Remote, mid-level ML role with common Data Scientist title attracts many qualified applicants.
Specialized fraud and payments experience required, limiting cross-industry transferability.
Requires 3+ years, fraud/payments experience, and production ML deployment skills.
Develop and maintain production-grade machine learning models and anomaly detection systems to detect and mitigate fraud across the platform.
Design and run experiments to balance customer experience with fraud loss reduction and size fraud typologies for product and investment prioritization.
Collaborate cross-functionally with fraud operations, engineers, product managers, and analysts to translate model outputs into actionable fraud interventions.
Degree in a quantitative field such as Statistics, Mathematics, Engineering, or Computer Science.
3+ years of experience in data science, decision science, or risk analytics focused on fraud, payments, or financial crime.
Proficiency in Python and SQL with experience building and deploying machine learning models in production.
Work Experience Required: 3+ years in relevant fraud or financial crime data science roles.
Strong foundation in statistics and data science fundamentals including experimentation, statistical inference, model evaluation, and feature engineering.
Experience working in fast-paced, cross-functional teams with high ownership and comfort communicating technical findings to non-technical stakeholders.
Background in fraud, risk, or financial services data science with investigative instincts for uncovering novel fraud patterns.