Product Manager - Anti Fraud

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams

We run a large-scale GPU cloud and AI infrastructure platform. Fraud and abuse - stolen payment cards, fake sign-ups, compute mining, credential abuse, and coordinated free-tier exploitation - directly threaten platform economics, capacity availability for legitimate customers, and our standing with payment providers and cloud partners.

As Product Manager, Anti-Fraud, you own the product roadmap for the systems that detect, prevent, and respond to fraud and abuse across the customer lifecycle - from onboarding and payment through to ongoing consumption. You sit at the intersection of Trust & Safety, Payments/Billing, Risk, Data Science, and Product Engineering, and you balance two goals in constant tension: stop bad actors and keep friction near zero for real customers.

• Own the end-to-end anti-fraud product roadmap: signup/KYC checks, payment fraud detection, account takeover protection, and compute-abuse (e.g., crypto mining, resource reselling) prevention.
• Own the rules and risk-scoring surfaces - the tooling that lets analysts and data scientists define, test, and deploy detection rules and ML models with clear guardrails and audit trails.
• Own the manual review and case-management experience for the Trust & Safety operations team, including queues, evidence views, and action workflows (block, limit, step-up verification, release).
• Own customer-facing friction points - verification flows, step-up challenges, and appeals - designed to minimize false positives and protect the legitimate user experience.
• Identify fraud and abuse incidents early - define the signals, alerts, and thresholds that surface incidents before they scale, and own the detection surface that flags them.
• Participate in incident reviews and post-mortems, capturing root causes and turning each finding into concrete improvements to detection rules, models, and the roadmap.
• Ensure timely response to active incidents - drive fast triage, clear ownership, and mitigation, working alongside Trust & Safety, Engineering, and Data Science to contain impact.
• Write clear PRDs and specs that translate fuzzy risk problems into shippable engineering and data-science work.
• Partner with Analytics team on detection models - defining the problem, labels, and success criteria, and deciding where rules vs. ML are the right tool.
• Run experiments and monitor rollouts, watching both fraud-catch metrics and false-positive / customer-friction impact.
• Work with Legal/Compliance to keep detection and enforcement aligned with payment-network rules, KYC/AML expectations, and data-privacy requirements.
• Track and report on fraud loss, chargeback rate, false-positive rate, review throughput, and time-to-detection.

Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI