Product Manager - Anti Fraud

Accountabilities: Own the anti-fraud product roadmap: Define and execute the end-to-end strategy for fraud and abuse prevention across customer onboarding, identity verification, payments, account security, and ongoing resource consumption. Develop fraud detection capabilities: Lead product initiatives covering stolen payment cards, fraudulent registrations, account takeovers, credential abuse, unauthorized compute usage, cryptocurrency mining, resource reselling, and free-tier exploitation. Manage risk rules and scoring systems: Own the tools and workflows that enable analysts and data scientists to define, test, deploy, and monitor detection rules and machine learning models, with appropriate safeguards and audit trails. Improve Trust & Safety operations: Design and optimize manual review and case-management tools, including investigation queues, evidence views, and workflows for blocking accounts, limiting access, requesting additional verification, and releasing legitimate users. Optimize customer verification experiences: Develop customer-facing verification, step-up authentication, and appeals processes that reduce false positives while maintaining effective protection against malicious activity. Strengthen fraud monitoring and incident detection: Define risk signals, alerting mechanisms, thresholds, and detection workflows to identify emerging incidents early and prevent fraud from escalating. Lead incident response and continuous improvement: Coordinate with Trust & Safety, Engineering, and Data Science teams to triage active incidents, establish ownership, mitigate risks, and participate in post-incident reviews. Translate risk challenges into product solutions: Write clear product requirements documents (PRDs) and technical specifications that transform ambiguous fraud and abuse problems into actionable engineering and data science deliverables. Collaborate on detection models: Partner with Analytics and Data Science teams to define detection problems, establish data labels and success criteria, and determine when rule-based systems or machine learning approaches are most appropriate. Run experiments and monitor performance: Plan experiments, oversee product rollouts, and evaluate detection effectiveness alongside false-positive rates and the impact of security measures on customer experience. Ensure regulatory and payment compliance: Collaborate with Legal and Compliance teams to align fraud prevention and enforcement mechanisms with payment-network requirements, KYC/AML obligations, and data privacy standards. Track and communicate key metrics: Monitor and report on fraud losses, chargeback rates, false-positive rates, review throughput, and time to detection, using insights to guide prioritization and product improvements. Requirements Product management experience: Proven experience in product management within fraud prevention, risk management, Trust & Safety, payments, or a closely related field. Strong analytical capabilities: Comfortable working with data, using SQL or equivalent analytical tools, and interpreting performance metrics to guide product decisions. Fraud and risk expertise: Understanding of payment fraud, chargebacks, identity verification, KYC/AML processes, and risk signals such as device fingerprinting, transaction velocity, IP reputation, and network behavior. Technical product knowledge: Ability to collaborate effectively with Engineering and Data Science teams and engage in technical discussions about detection signals, data features, system behavior, and model performance. Data-driven decision-making: Familiarity with precision and recall trade-offs, detection effectiveness, false-positive management, and the balance between security and customer experience. Experience in relevant technology environments: Exposure to cloud infrastructure, SaaS, marketplaces, fintech, or other technology-driven platforms with complex risk and operational requirements. Strategic thinking and prioritization: Ability to make sound decisions in ambiguous situations, assess competing priorities, and balance security, business performance, compliance, and user experience. Cross-functional collaboration: Strong communication and stakeholder management skills, with the ability to align technical and non-technical teams around shared goals and measurable outcomes. Incident management mindset: Ability to respond effectively to emerging threats, prioritize urgent issues, coordinate mitigation efforts, and turn incident findings into lasting product improvements. Ownership and adaptability: A proactive, results-oriented approach, with the ability to take responsibility for complex product areas in a fast-paced, evolving environment. The following qualifications are considered an advantage: Machine learning detection systems: Familiarity with ML-based fraud detection and the operational lifecycle of models, including monitoring, drift detection, and retraining. Cloud and compute abuse prevention: Experience addressing abuse patterns specific to cloud or GPU platforms, including unauthorized mining, credential stuffing, account farming, and systematic exploitation of free-tier resources. Risk tooling and operational workflows: Experience building internal investigation tools, risk-scoring systems, automated enforcement workflows, or fraud operations platforms. Benefits Competitive compensation: A competitive compensation package aligned with the role and experience. Career development: Opportunities to develop your product management expertise and grow professionally in a rapidly evolving technology sector. Flexibility and ownership: A working environment that encourages autonomy, initiative, and responsibility for meaningful product decisions. Collaborative culture: Work alongside talented professionals across Product, Engineering, Data Science, Risk, and Trust & Safety. Impactful technology: Contribute to AI infrastructure and develop solutions that protect critical cloud resources and legitimate customers. International environment: Collaborate with diverse teams and stakeholders in a global technology setting. Continuous learning: Explore emerging fraud prevention techniques, machine learning applications, and new approaches to cloud security. Meaningful responsibility: Help shape product strategy and influence how fraud and abuse are detected, investigated, and prevented at scale.