Fraud Patterns Analyst

Details

Stripe is a financial infrastructure platform for businesses. The Risk Operations team is seeking an experienced fraud analyst to join its global fraud operations team. This role focuses on writing and maintaining fraud rulesets, conducting advanced data analysis to identify and mitigate transaction fraud, minimizing user impact, and collaborating with fraud, operations, product, engineering, and data science stakeholders.

Responsibilities

• Build and maintain fraud rulesets to prevent transaction-level fraud losses, including monitoring and measuring precision and recall.
• Conduct advanced analysis of structured and unstructured data to identify emerging fraud attacks.
• Collaborate with product, risk, and operations teams to identify and mitigate fraud exposure.
• Investigate complex and distributed fraud attacks, perform root cause analysis, and deploy remediations.
• Investigate anomalous transaction clusters using account activity, processing volume, and other risk indicators while minimizing negative user impact.
• Respond to incidents involving complex fraud schemes.
• Use analytics to automate manual processes and workloads.
• Create visualizations, dashboards, and queries covering impact, performance, loss risks, and user experience.
• Use Stripe tools and systems to take systematic action against fraudulent merchants while maintaining a high level of accuracy.

Requirements

• At least five years of experience conducting advanced data analysis and managing transaction fraud rulesets.
• Advanced proficiency in SQL.
• Experience working with modeling, data science, and intelligence stakeholders to implement automated and scalable controls and processes.
• Experience creating data visualizations and dashboards and presenting findings to technical and non-technical audiences, including senior leadership.
• Ability to drive execution on projects in a highly cross-functional environment.
• Creativity, teamwork, effective problem-solving skills, and a willingness to question the status quo.
• A user-focused, pragmatic, and solutions-oriented approach.

Preferred Qualifications

• Fraud experience in payments, e-commerce, fintech, or cryptocurrency, including digital and card-not-present fraud.
• Experience investigating and mitigating card testing and account takeover attacks.
• Proficiency in Splunk, Python, Tableau, or other data visualization tools.
• An undergraduate or advanced degree in analytics, data science, or statistics.
• Experience with clustering, classification, and link analysis.
• Experience in fast-paced and rapidly changing environments.
• Experience designing and implementing product-level fraud and risk controls.