Product Manager - Anti-Fraud
J
JobgetherCloud infrastructure
Based in NetherlandsFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- SQLRisk Management
Requirements
- Have product management experience in fraud prevention, risk management, Trust & Safety, payments, or a closely related field.
- Work with data using SQL or equivalent analytical tools and interpret performance metrics to guide product decisions.
- Understand payment fraud, chargebacks, identity verification, KYC/AML processes, and risk signals.
- Understand risk signals such as device fingerprinting, transaction velocity, IP reputation, and network behavior.
- Collaborate effectively with Engineering and Data Science and discuss detection signals, data features, system behavior, and model performance.
- Understand precision and recall trade-offs, detection effectiveness, false-positive management, and security/customer-experience balance.
- Have exposure to cloud infrastructure, SaaS, marketplaces, fintech, or other technology-driven platforms with complex risk and operational requirements.
- Prioritize in ambiguous situations and balance security, business performance, compliance, and user experience.
- Align technical and non-technical stakeholders around shared goals and measurable outcomes.
- Respond to emerging threats, coordinate mitigation efforts, and turn incident findings into product improvements.
- Familiarity with machine-learning fraud detection and model monitoring, drift detection, and retraining is an advantage.
- Experience with cloud or GPU abuse prevention, risk tooling, investigation tools, risk-scoring systems, or fraud operations platforms is an advantage.
Responsibilities
- Define and execute the end-to-end fraud and abuse prevention product roadmap across onboarding, identity verification, payments, account security, and resource consumption.
- Lead product initiatives addressing payment fraud, fraudulent registrations, account takeovers, credential abuse, unauthorized compute usage, cryptocurrency mining, resource reselling, and free-tier exploitation.
- Own tools and workflows for analysts and data scientists to define, test, deploy, and monitor detection rules and machine learning models.
- Design and optimize manual review and case-management tools, including investigation queues, evidence views, and enforcement workflows.
- Develop customer verification, step-up authentication, and appeals processes that reduce false positives while protecting against malicious activity.
- Define risk signals, alerts, thresholds, and detection workflows to identify emerging incidents.
- Coordinate incident triage, risk mitigation, ownership, and post-incident reviews with Trust & Safety, Engineering, and Data Science.
- Write product requirements documents and technical specifications that turn fraud and abuse problems into engineering and data science deliverables.
- Partner with Analytics and Data Science to define detection problems, data labels, success criteria, and appropriate rule-based or machine learning approaches.
- Plan experiments and product rollouts, monitor detection performance and false-positive rates, align prevention with compliance requirements, and report fraud and operational metrics.
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