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Remote role plus mid-level experience raises applicant volume despite niche fraud specialization.
Specialized fraud, payments and financial-crime experience limits transferability across industries.
Mandatory 5+ years in fraud/risk with production ML and Python/SQL makes filters moderately strict.
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Develop, prototype, and productionize machine learning models specifically for fraud detection, owning their monitoring and retraining.
Design and execute experiments to measure fraud intervention impacts while balancing customer experience and loss reduction.
Collaborate cross-functionally with engineering, product, fraud operations, and analytics teams to translate model insights into active fraud mitigation systems.
5+ years experience in data science, decision science, or risk analytics in fraud, payments, or financial crime domains.
Degree in a quantitative field such as Statistics, Mathematics, Engineering, or Computer Science.
Strong proficiency in Python and SQL with hands-on experience in end-to-end ML model development and deployment in production.
Solid understanding of statistics, experimentation, statistical inference, model evaluation, and feature engineering.
Experienced in the financial crime or fraud risk domain with a track record of building production-grade fraud detection systems.
Able to balance technical depth in data science with cross-functional collaboration to impact real-world fraud prevention actions.
Comfortable working in a fast-paced environment requiring high ownership and clear communication of complex technical findings to non-technical stakeholders.