The role

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. 

Your responsibilities will include: 

• 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. 

We expect you to have: 

• Proven product management experience in fraud, risk, trust & safety, or payments. 

• Strong analytical skills — comfortable with data, SQL or equivalent, and reasoning about precision/recall trade-offs and metrics. 

• Ability to make decisions under ambiguity and to balance security, business, and customer-experience priorities. 

• Solid collaboration with engineering and data science; you can hold a technical conversation about signals, features, and model behavior. 

• Exposure to cloud, SaaS, marketplace, fintech, or infrastructure products. 

• Understanding payment fraud, chargebacks, KYC/AML, or device/network signals (fingerprinting, velocity, IP reputation). 

It would be an added bonus if you have: 

• Familiarity with ML-based detection systems and their operational lifecycle (drift, retraining, monitoring). 

• Experience with abuse patterns specific to compute or GPU platforms (mining, credential stuffing, free-tier farming).