Fraud and Risk Specialist
Investigates fraud, analyzes emerging patterns and false positives, and develops operational and analytical controls to reduce financial and reputational risk. The role requires a bachelor's degree, 1–2 years of fraud or risk monitoring experience, strong analytical judgment, and basic SQL knowledge.
About the job
Responsibilities
- Conduct daily reviews of fraud queues to minimize financial loss and meet service-level objectives.
- Own performance outcomes by clarifying objectives and formulating proactive solutions using sound business judgment.
- Serve as a rotating on-call specialist managing complex internal escalations across multiple communication channels.
- Analyze and monitor false-positive trends to refine business rules and fraud detection models.
- Collaborate with Analytics, Customer Experience, and Trust stakeholders to resolve false positives and protect legitimate customers.
- Partner with leadership to strengthen fraud protections, reduce friction, and improve customer experience.
Requirements
- Bachelor's degree, preferably in Computer Science, Statistics, Operations, Business Administration, or another analytical field.
- 1–2 years of experience in fraud or risk monitoring at a payments company, bank, or online marketplace.
- Ability to identify patterns, solve problems, and recognize anomalous activity.
- Detail-oriented, quantitatively driven, and operations-savvy.
- Strong time-management skills and ability to work effectively in a fast-paced environment.
- Experience collaborating across teams to build capabilities and implement projects.
Nice to Have
- Beginner to intermediate SQL experience.
Compensation
- Expected base pay range: $49,200–$61,500 annually, excluding potential equity, bonus, and benefits.
- Medical, dental, vision, mental health, family-building, childcare, pet, retirement, commuter, parental-leave, paid-time-off, holiday, Lyft credit, and Lyft Pink benefits.
Skills
Fraud Detection, Risk Monitoring, Fraud Analysis, Data Analytics, SQL, Business Rules, Fraud Models, Pattern Recognition, Financial Risk, Operations
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