[All remote jobs](https://jobicy.com/jobs.md)[![Nebius logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/06/d90c0566-221.webp)](https://jobicy.com/company/nebius.md)[Nebius](https://jobicy.com/company/nebius.md) # Product Manager – Anti Fraud Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/nebius.md)ShareRemote from[Europe](https://jobicy.com/job-region/europe.md)SalaryUndisclosedDepartment[Product & Operations](https://jobicy.com/categories/management.md)EmploymentFull TimeExperienceOpen levelPublished23 Oct 2026Apply before22 Nov 2026Listing views36Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Nebius is seeking a Product Manager to lead anti-fraud capabilities for its GPU cloud and AI infrastructure platform. The role owns the roadmap for fraud prevention across signup, KYC, payments, account security, compute abuse, manual review, and customer appeals. It requires close collaboration with Trust & Safety, Payments, Engineering, Data Science, Analytics, Legal, and Compliance teams. The successful candidate will balance fraud-loss reduction with low customer friction through rules, risk scoring, ML-supported detection, experiments, and incident response. This is a high-impact product role in a fast-moving technical environment where ambiguous risk problems must be translated into clear product requirements and measurable outcomes. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis role combines complex fraud-domain expertise with technical product management, data analysis, payment risk, and cloud-abuse prevention. The PM must make high-stakes trade-offs between security controls, false positives, customer experience, and platform economics during both roadmap work and live incidents. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$165,000US market range (per year)$130k–$210k0$231.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The annual cadence and USD market estimate are inferred for a senior, full-time anti-fraud product management role in the US market; actual compensation will vary by country, seniority, and equity structure. ## Core skills Skills and capabilities most closely associated with this opportunity. [Product management](https://jobicy.com/jobs?search_keywords=Product%20management.md)[Fraud prevention](https://jobicy.com/jobs?search_keywords=Fraud%20prevention.md)[Trust and safety](https://jobicy.com/jobs?search_keywords=Trust%20and%20safety.md)[Payment fraud](https://jobicy.com/jobs?search_keywords=Payment%20fraud.md)[Risk scoring](https://jobicy.com/jobs?search_keywords=Risk%20scoring.md)[KYC and AML](https://jobicy.com/jobs?search_keywords=KYC%20and%20AML.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Data analytics](https://jobicy.com/jobs?search_keywords=Data%20analytics.md)[Machine learning](https://jobicy.com/jobs?search_keywords=Machine%20learning.md)[Incident response](https://jobicy.com/jobs?search_keywords=Incident%20response.md)[Experimentation](https://jobicy.com/jobs?search_keywords=Experimentation.md)[Chargeback management](https://jobicy.com/jobs?search_keywords=Chargeback%20management.md)[Cloud security](https://jobicy.com/jobs?search_keywords=Cloud%20security.md)[Cross-functional leadership](https://jobicy.com/jobs?search_keywords=Cross-functional%20leadership.md) Sample interview questionsHow would you prioritize fraud controls when stronger prevention also increases legitimate-user friction?I would define the decision using fraud-loss reduction, false-positive rate, conversion impact, review volume, and operational cost. I would segment users and risk levels, test controls incrementally, and apply step-up verification to high-risk cohorts rather than imposing broad friction on every customer. Describe how you would decide whether to use rules or a machine-learning model for a new fraud pattern. I would start with the volume, stability, explainability needs, available labels, and cost of false positives. Rules are effective for clear and rapidly changing signatures, while ML is more valuable when multiple weak signals interact and enough quality historical data exists. I would also plan monitoring, auditability, rollback procedures, and periodic retraining before launch. What metrics would you use to evaluate an anti-fraud product? I would track prevented fraud loss, chargeback rate, fraud-catch rate, precision, recall, false-positive rate, approval or conversion rate, time to detection, time to mitigation, review throughput, and appeal outcomes. Metrics should be broken down by customer segment, payment method, geography, and attack type to expose uneven impact. How would you respond to an active compute-abuse incident such as coordinated crypto mining? I would establish an incident owner, assess affected accounts and infrastructure, and apply immediate containment such as rate limits, account restrictions, payment holds, or access blocks. In parallel, I would coordinate engineering, Trust and Safety, and data teams to identify common signals, monitor evasion, communicate decisions, and convert the post-incident findings into durable detection and workflow improvements. How do you turn a vague risk concern into a shippable product requirement? I begin by defining the threat, affected user journey, assumptions, measurable success criteria, and acceptable trade-offs. I then document the required signals, decision logic, user actions, operational workflows, edge cases, privacy considerations, and instrumentation in a PRD, aligning engineering, analytics, operations, and legal stakeholders before delivery. Opportunity details ## About this role. About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. ### 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). Benefits & Perks: * 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 What’s it like to work at Nebius: Fast moving – Bold thinking – Constant growth – Meaningful impact – Trust and real ownership – Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know. Show more [Apply now >](https://jobicy.com/jobs/156525-product-manager-anti-fraud.md) > Annual salary information is not provided for this position. 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