Product Manager - Anti Fraud

Description

The company is building a full-stack AI cloud platform that takes developers and enterprises from data and model training through to production deployment, so they do not need to build large in-house AI and ML infrastructure.

The company builds its own systems across compute, storage, networking and applied AI, including large-scale GPU orchestration and inference optimization.

The company is listed on Nasdaq (NBIS), is headquartered in Amsterdam, and has R&D hubs in Europe, the UK, North America and Israel. The team has over 1,500 people, including hundreds of engineers working across hardware, software and AI R&D.

Responsibilities

• The company runs a large GPU cloud and AI infrastructure platform. Fraud and abuse, such as stolen payment cards, fake sign-ups, compute mining, credential abuse and coordinated free-tier exploitation, put platform economics, capacity for legitimate customers and relationships with payment providers and cloud partners at risk.
• As Product Manager, Anti-Fraud, you set the product roadmap for the systems that detect, prevent and respond to fraud and abuse across the customer lifecycle, from onboarding and payment to ongoing usage. You work across Trust & Safety, Payments and Billing, Risk, Data Science and Product Engineering, and you weigh stopping bad actors against keeping friction close to zero for real customers.
• You own the end-to-end anti-fraud roadmap: signup and KYC checks, payment fraud detection, account takeover protection, and prevention of compute abuse such as crypto mining and resource reselling.
• You own the rules and risk-scoring tools that let analysts and data scientists define, test and deploy detection rules and ML models, with clear guardrails and audit trails.
• You own the manual review and case management experience for the Trust & Safety operations team, including queues, evidence views and actions such as block, limit, step-up verification and release.
• You own customer-facing friction points, including verification flows, step-up challenges and appeals, and design them to cut false positives and protect the experience for legitimate users.
• You define the signals, alerts and thresholds that catch incidents before they grow, and you own the detection surface that flags them.
• You take part in incident reviews and post-mortems, identify root causes, and turn each finding into concrete changes to rules, models and the roadmap.
• You make sure active incidents get a fast response by driving triage, clear ownership and mitigation with Trust & Safety, Engineering and Data Science to contain the impact.
• You write clear PRDs and specs that turn unclear risk problems into work that engineering and data science can ship.
• You work with the Analytics team on detection models, defining the problem, labels and success criteria, and deciding when rules or ML is the better tool.
• You run experiments and monitor rollouts, tracking both fraud-catch metrics and the effect on false positives and customer friction.
• You work with Legal and Compliance to keep detection and enforcement consistent with payment network rules, KYC and AML expectations and data privacy requirements.
• You track and report on fraud loss, chargeback rate, false-positive rate, review throughput and time to detection.

Requirements

• You have proven product management experience in fraud, risk, trust and safety, or payments.
• You are analytically strong, comfortable with data and SQL or equivalent tools, and can reason about precision, recall and the trade-offs between them.
• You can make decisions when the picture is unclear and balance security, business and customer experience priorities.
• You collaborate well with engineering and data science and can discuss signals, features and model behavior in technical detail.
• You have exposure to cloud, SaaS, marketplace, fintech or infrastructure products.
• You understand payment fraud, chargebacks, KYC and AML, or device and network signals such as fingerprinting, velocity checks and IP reputation.
• Bonus: you know ML-based detection systems and their operational lifecycle, including drift, retraining and monitoring.
• Bonus: you have worked with abuse patterns specific to compute or GPU platforms, such as mining, credential stuffing and free-tier farming.

Conditions

The posting does not state location, work model, contract type, salary or schedule details.

Benefits

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• A collaborative, inventive culture
• The chance to work on AI projects with real impact
• An international environment with strong teams
• The company describes its working style as fast moving, bold in its thinking, constantly growing, focused on meaningful impact, built on trust and real ownership, and offering a chance to help shape the future of AI.