Product Manager - Anti Fraud

Nebius Group

Amsterdam

À distance

EUR 80 000 - 120 000

Plein temps

Il y a 3 jours
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Avantages offerts par ce poste

Competitive compensation
Career growth and learning
Flexibility and ownership
Collaborative culture
Impactful AI projects

Résumé du poste

Nebius Group is seeking a Product Manager for Anti-Fraud to own and evolve the detection, prevention, and response systems across the customer lifecycle. You will interface with Trust & Safety, Payments, Risk, Data Science, and Product Engineering to balance stopping fraud with keeping real customers’ experience smooth.

Responsibilities include shaping the anti-fraud roadmap, defining detection rules and ML model guardrails, and coordinating with analytics to improve detection accuracy while

Qualifications

  • Proven product management experience in fraud, risk, trust & safety, or payments.
  • Strong analytical skills with data, SQL or equivalent.
  • Decision making under ambiguity balancing security, business and user experience priorities.

Responsabilités

  • Own the end-to-end anti-fraud product roadmap across onboarding, payments, and usage.
  • Define rules and risk-scoring surfaces with guardrails and audit trails.
  • Oversee manual review, queues, evidence views and action workflows.

Connaissances

Fraud product mgmt
Data analysis
Decision making
Collaboration

Outils

SQL

Description du poste

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.

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.

  • Remote - Europe

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

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.

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