Fraud Architect

Stripe

. Own each assigned user’s prevention strategy, including risk baseline, threat model, and prevention plan .

Posted 10/6/2026full-timeRemote • United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in fraud prevention strategies, including risk assessment, threat modeling, and the development of effective controls. Proficient in SQL, Python, and machine learning applications for fraud detection and prevention.

Highest-signal resume keywords

Fraud InvestigationPayment Fraud AnalysisAPI IntegrationSQL ProficiencyMachine Learning Risk Decisions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Fraud Prevention StrategyRisk Baseline AssessmentThreat ModelingRule DevelopmentSignal Flow AnalysisData Quality AssessmentHypothesis TestingPattern InvestigationControl EvaluationTechnical Engagement Management

Soft Skills

Strong CommunicationProduct Judgment

Tools & Technologies

Stripe RadarVAMPECMEFMFraud Detection Platforms

Industry Keywords

PaymentsFintechRisk ManagementFraud PreventionAccount TakeoverMulti-AccountingDispute ManagementUsage-Based Billing

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Own each assigned user’s prevention strategy, including risk baseline, threat model, and prevention plan
Investigate Radar scoring, classification, rule behavior, and signal coverage using transaction evidence and technical context
Explain fraud evidence and uncertainty and recommend configuration, integration, model, or feature actions
Help users configure and optimize Radar; develop and validate rules, thresholds, and integration recommendations
Investigate payment, account, and behavioral fraud patterns, including payment fraud, trial and promotion abuse, multi-accounting, bot activity, and usage-based billing exploitation
Run weekly risk-health reviews and share concise health updates
Establish early-warning thresholds and notification paths; translate fraud, dispute, approval-rate, and false-positive changes into prevention actions
Maintain action plans with owners, due dates, expected impact, and implementation status
Coordinate incident updates, conduct merchant-specific root-cause analysis, and verify prevention effectiveness
Partner with Radar Product, Engineering, and Fraud Data Science to investigate limitations and define requirements
Maintain a cross-user backlog of signal, model, integration, and payment-method gaps
Build playbooks, investigation tools, and prevention frameworks; train Customer Success Managers, Technical Account Managers, and Account Executives
Shape account segmentation, coverage expectations, tooling, hiring, incident handoffs, and program measurement

Requirements

What you’ll need

8+ years in a technical role with substantial direct user engagement 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
Depth 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 and 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
SQL proficiency and experience using Python, R, or a similar language for hypothesis testing, pattern investigation, and control evaluation
Ability to assess data quality, delayed fraud outcomes, precision, recall, false positives, and business impact
Experience managing multiple user relationships or technical engagements and driving recommendations through implementation and measured results
Strong communication and product judgment
Preferred: experience with Stripe Radar or similar platforms such as Forter, Sift, Ravelin, or Signifyd
Preferred: experience designing, testing, and tuning fraud rules or thresholds in production
Preferred: experience partnering with product managers, engineers, and data scientists
Preferred: familiarity with device intelligence, alternative payment methods, abuse prevention beyond payments, or usage-based business models
Preferred: experience building and scaling a technical advisory function or multi-account service model