Product Manager - Anti-Fraud
N
NebiusCloud infrastructure
Remote - EuropeFull-Time
Salary not disclosed
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Job Details
- Required Skills
- SQLProduct ManagementRisk Management
Requirements
- Have product management experience in fraud, risk, trust & safety, or payments.
- Bring strong analytical skills and comfort working with data and SQL or an equivalent.
- Be able to reason about precision/recall trade-offs and metrics.
- Make decisions under ambiguity and balance security, business, and customer-experience priorities.
- Collaborate with engineering and data science and hold technical conversations about signals, features, and model behavior.
- Understand payment fraud, chargebacks, KYC/AML, or device/network signals such as fingerprinting, velocity, and IP reputation.
- Have exposure to cloud, SaaS, marketplace, fintech, or infrastructure products.
- Familiarity with ML-based detection systems and their operational lifecycle, including drift, retraining, and monitoring, is a bonus.
- Experience with compute- or GPU-platform abuse patterns, including mining, credential stuffing, or free-tier farming, is a bonus.
Responsibilities
- Own the end-to-end anti-fraud product roadmap for signup and KYC checks, payment fraud detection, account takeover protection, and compute-abuse prevention.
- Own the rules and risk-scoring tools used by analysts and data scientists to define, test, and deploy detection rules and ML models with guardrails and audit trails.
- Own the manual-review and case-management experience for Trust & Safety operations, including queues, evidence views, and action workflows.
- Design customer-facing verification, step-up challenge, and appeals flows to reduce false positives and protect the legitimate customer experience.
- Define signals, alerts, and thresholds to identify fraud and abuse incidents early.
- Participate in incident reviews and post-mortems, and turn root causes into improvements to detection rules, models, and the roadmap.
- Drive triage, ownership, and mitigation for active incidents with Trust & Safety, Engineering, and Data Science.
- Write PRDs and specifications that translate risk problems into engineering and data-science work.
- Partner with Analytics on detection models, defining problems, labels, success criteria, and when to use rules versus ML.
- Run experiments and monitor rollouts, tracking fraud-catch metrics and false-positive and customer-friction impact.
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