Fraud Architect

The shape of it

In the posting’s own words

The Fraud Architect team helps Stripe’s largest and most complex users manage fraud and abuse across the customer lifecycle. We combine deep fraud expertise, technical investigation, and product knowledge to help users understand emerging threats, optimize Radar, and implement durable prevention strategies tailored to their business and risk tolerance. Our goal is to enable users’ growth by proactively reducing fraud while protecting legitimate customer activity and turning frontline insights into better products and defenses.

What it asks for · 8

• 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. You can reason about adversarial behavior, payment flows, and the tradeoffs between fraud prevention and legitimate user activity.
• 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 depth to understand API integrations, trace data and signal flows, investigate system behavior, and discuss technical limitations and implementation options with engineers. You can connect those details to a user’s business problem.
• Practical understanding of machine-learning-based risk decisions and rule systems, including how signal availability, model behavior, rules, and thresholds interact. You can explain findings clearly without overstating what the evidence establishes.
• Strong investigative and data science fundamentals: SQL proficiency and experience using Python, R, or a similar language to test hypotheses, investigate unfamiliar patterns, and evaluate controls. You can 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 maintaining proactive coverage and driving recommendations through implementation and measured results.
• Strong communication and product judgment. You can explain fraud exposure to a CFO, discuss rule logic and integration behavior with an engineer, and turn a user problem into a clear, prioritized product requirement.

Also a plus

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

What the job covers

• Own each user’s prevention strategy. Maintain a current risk baseline, threat model, and agreed prevention plan for every assigned account. Understand payment methods, integrations, billing flows, trial and promotion mechanics, existing controls, and risk preferences; identify the top risks and gaps across the full customer lifecycle.
• Make Radar’s behavior understandable and actionable. Investigate scoring, classification, rule behavior, and signal coverage using transaction evidence and technical context. Explain observed behavior and uncertainty clearly, distinguish configuration or integration issues from potential model or feature gaps, and recommend the right next step for the user and our product teams.
• Design and improve user protections. Help users configure and optimize Radar for their business model and risk tolerance. Develop and validate user-facing rules, thresholds, and integration recommendations, test their expected impact, and guide approved implementation. Balance fraud prevention with legitimate payment acceptance and false-positive risk, without taking on internal-rule deployment or model operations.
• Anticipate established and emerging fraud vectors. Investigate payment, account, and behavioral patterns to understand how abuse works and where it may move next. Advise on payment fraud, trial and promotion abuse, multi-accounting, bot activity, and usage-based billing exploitation. Evaluate defenses across payment methods and flows rather than treating each attack surface in isolation.
• Deliver consistent proactive coverage. Run a weekly risk-health review for every assigned account, including those without active incidents, and share a concise health update. Establish early-warning thresholds and notification paths; translate changes in fraud, disputes, early fraud warnings, approval rates, and false positives into timely user conversations and prevention actions. Lead deeper periodic reviews with technical and business stakeholders.
• Own follow-through, not just recommendations. Maintain a user action plan with named owners, due dates, expected impact, and implementation status. Follow recommendations through user acceptance, implementation, and verification of effectiveness. Revisit residual risk and adjust controls as traffic, threats, and user priorities change.
• Connect incident response to durable prevention. During incidents, provide user context and prioritization, coordinate user-facing updates with the account team, conduct merchant-specific root-cause analysis, synthesize findings with Engineering, and translate them into an implemented prevention plan. Verify that prevention measures are implemented and effective after handoff.
• Turn user evidence into product improvements. Partner with Radar Product, Engineering, and Fraud Data Science to investigate limitations and define actionable requirements. Maintain a cross-user backlog of signal, model, integration, and payment-method gaps with clear owners and follow-up milestones. Advocate for durable fixes, explain progress to users, and help them adopt and validate delivered improvements.
• Enable others to act. Build reusable playbooks, investigation tools, and prevention frameworks. Train Customer Success Managers, Technical Account Managers, and Account Executives to recognize common fraud patterns, explain risk tradeoffs, and know when to involve a specialist.
• Build and evolve the program. Shape account segmentation, coverage expectations, tooling, and hiring as the function grows. Work with leadership and partner teams to protect proactive capacity and establish clear incident handoffs. Measure success through current prevention plans and consistent coverage across the portfolio, recommendations implemented and proven effective, and improvements in user risk outcomes—not the volume of analysis produced.

Tools and skills named

Words the posting leans on

• user30×
• fraud25×
• prevention16×
• risk13×
• technical12×
• payment11×
• product11×
• account10×
• model10×
• abuse8×
• business8×
• integration8×
• behavior7×
• experience7×
• explain7×
• radar7×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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