Fraud Architect

Details

Stripe's Fraud Architect team helps large and complex users manage fraud and abuse across the customer lifecycle. The team combines fraud expertise, technical investigation, and product knowledge to help users understand emerging threats, optimize Radar, and implement durable prevention strategies.

Responsibilities

• Own each user's fraud-prevention strategy, including risk baselines, threat models, prevention plans, payment methods, integrations, billing flows, trial and promotion mechanics, controls, and risk preferences.
• Investigate Radar scoring, classifications, rules, and signal coverage using transaction evidence and technical context.
• Explain observed behavior and uncertainty, distinguish configuration or integration issues from model or feature gaps, and recommend appropriate next steps.
• Help users configure and optimize Radar by developing and validating rules, thresholds, and integration recommendations.
• Investigate payment, account, and behavioral abuse, including payment fraud, trial and promotion abuse, multi-accounting, bot activity, and usage-based billing exploitation.
• Conduct weekly risk-health reviews, establish early-warning thresholds and notification paths, and translate fraud, dispute, early fraud warning, approval-rate, and false-positive changes into prevention actions.
• Maintain user action plans with owners, due dates, expected impact, and implementation status, and verify that recommendations are implemented and effective.
• Support incident response through user context, prioritization, coordination with account teams, merchant-specific root-cause analysis, and prevention planning.
• Partner with Radar Product, Engineering, and Fraud Data Science to investigate limitations and define requirements for signal, model, integration, and payment-method improvements.
• Build reusable playbooks, investigation tools, and prevention frameworks, and train Customer Success Managers, Technical Account Managers, and Account Executives on fraud patterns and risk tradeoffs.
• Help shape account segmentation, coverage expectations, tooling, incident handoffs, and the growth of the Fraud Architect program.

Requirements

• 8+ years in a technical role with substantial direct user engagement, such as solutions architecture, technical account management, fraud consulting, professional services, engineering, or technical product work at a payments company, fintech, risk management platform, or fraud-prevention provider.
• Hands-on experience investigating fraud or abuse and translating findings into effective controls or user guidance.
• Expertise in payment fraud, account takeover, subscription or trial abuse, multi-accounting, dispute management, and network monitoring programs such as VAMP, ECM, or EFM.
• Technical understanding of API integrations, trace data, signal flows, system behavior, technical limitations, and implementation options.
• Practical understanding of machine-learning-based risk decisions and rule systems, including signal availability, model behavior, rules, and thresholds.
• Strong investigative and data science fundamentals, including SQL proficiency and experience with Python, R, or a similar language.
• Ability to assess data quality, account for delayed fraud outcomes, and interpret precision, recall, false positives, and business impact.
• Experience managing multiple user relationships or technical engagements while driving recommendations through implementation and measured results.
• Strong communication and product judgment, including the ability to explain fraud exposure to executives, discuss rule logic and integration behavior with engineers, and turn user problems into prioritized product requirements.

Preferred Qualifications

• Experience with Stripe Radar or similar platforms such as Forter, Sift, Ravelin, or Signifyd.
• Experience designing, testing, and tuning fraud rules or thresholds in production, including shadow evaluation, user approval, and post-launch reviews.
• Experience partnering with product managers, engineers, and data scientists to diagnose model or signal gaps and deliver product improvements.
• Familiarity with device intelligence, alternative payment methods, abuse prevention beyond payments, or usage-based business models.
• Experience building and scaling technical advisory functions, reusable prevention practices, or multi-account service models.