Fraud Strategy Decision Scientist

What You’ll Do

• Own and drive fraud strategy for key risk areas across travel and expense, balancing fraud loss reduction with customer experience.
• Design and evolve fraud rules, thresholds, and decision workflows, informed by data science models, ML features, and investigative insights.
• Partner closely with Data Science teams to translate machine learning model outputs and features into effective, explainable fraud strategies.
• Lead strategy development across onboarding, payments, expense submissions, and transaction monitoring.
• Apply advanced analytics techniques (trend analysis, segmentation, clustering, network analysis) to identify emerging fraud patterns and control gaps.
• Perform root-cause analysis and loss attribution, quantifying financial impact and prioritizing strategy improvements.
• Own strategy performance metrics, including fraud loss, approval rates, false positives, and customer friction.
• Collaborate cross-functionally with Fraud Operations to ensure strategies are operationally executable and continuously optimized.
• Partner with Engineering and Product to implement fraud strategies into real-time and batch decisioning systems.
• Drive experimentation and A/B testing of rules, thresholds, and model-driven strategies.
• Contribute to the long-term fraud strategy roadmap, including tooling, rule engines, model integration, and automation.
• Support vendor evaluations and third-party data integrations to enhance detection signals.
• Mentor junior fraud strategists and analysts, setting best practices for strategy design, documentation, and governance.

What We’re Looking For

• 7–10+ years of experience in fraud strategy, fraud analytics, or financial crime risk management.
• Strong experience designing and managing fraud rules, policies, and decision strategies in production environments.
• Deep understanding of how machine learning models and features are used to inform fraud decisions.
• Proficiency in SQL and strong working knowledge of Python for analysis and strategy validation.
• Experience working with large-scale data platforms such as Snowflake, Databricks, Spark, or similar.
• Solid understanding of card payments, transaction flows, identity verification, and fraud typologies (ATO, synthetic identity, first-party fraud, third-party fraud, scams).
• Demonstrated ability to partner effectively with Data Science, Engineering, Product, and Fraud Operations teams.
• Strong analytical mindset with the ability to translate complex data into clear, actionable strategy decisions.
• Excellent communication skills, including presenting strategy recommendations to senior leadership.
• Experience in fintech, payments, travel, or e-commerce environments preferred.
• Bachelor’s degree in a quantitative or analytical field; Master’s degree preferred.