Fraud Architect

SF-HQ, Chicago, New York, US-Remote · US · 8+ years experience · Remote · Posted Oct 6, 2026

At a glance

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

The Fraud Architect team helps Stripe’s largest and most complex users manage fraud and abuse across the customer lifecycle.
As a member of the Fraud Architect team, you will act as the fraud and risk specialist embedded in strategic user relationships for a small portfolio of named users.
This role combines analytical rigor, deep fraud investigation, and product judgment to help users optimize Radar, build proactive prevention strategies, and turn complex fraud problems into practical decisions.

Own each user’s prevention strategy by maintaining a current risk baseline, threat model, and agreed prevention plan for every assigned account.
Make Radar’s behavior understandable and actionable by investigating scoring, classification, rule behavior, and signal coverage.
Design and improve user protections by helping users configure and optimize Radar for their business model and risk tolerance.
Anticipate established and emerging fraud vectors by investigating payment, account, and behavioral patterns.
Deliver consistent proactive coverage by running a weekly risk-health review for every assigned account and establishing early-warning thresholds.
Own follow-through by maintaining a user action plan with named owners, due dates, expected impact, and implementation status.
Connect incident response to durable prevention by conducting merchant-specific root-cause analysis and translating findings into an implemented prevention plan.
Turn user evidence into product improvements by partnering with Radar Product, Engineering, and Fraud Data Science.
Enable others to act by building reusable playbooks, investigation tools, and prevention frameworks and training internal teams.
Build and evolve the program by shaping account segmentation, coverage expectations, tooling, and hiring as the function grows.

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.
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.
Practical understanding of machine-learning-based risk decisions and rule systems, including how signal availability, model behavior, rules, and thresholds interact.
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.
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 to explain fraud exposure to a CFO, discuss rule logic with an engineer, and turn a user problem into a clear, prioritized product requirement.

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.