Product Manager - Anti Fraud

Jobgether; España; On-site; EUR 90,000 - 120,000; Full time

Benefits offered by this job

Competitive compensation

Career development

Flexibility and ownership

Collaborative culture

Impactful technology

International environment

Continuous learning

Meaningful responsibility

Job summary

Jobgether is seeking a Product Manager – Anti-Fraud in Spain to lead the development of fraud prevention products across onboarding, payments, and security. You will own risk scoring, detection models, and incident response, coordinating with Analytics, Engineering, and Legal to ensure compliant, scalable solutions that protect customers.

The role requires strong product instincts, analytical rigor, and experience in cloud/SaaS or fintech environments.

Qualifications

• Proven product management experience in fraud prevention, risk, trust & safety, payments, or related field.
• Strong analytical capabilities with data interpretation to drive decisions.
• Understanding of payment fraud, KYC/AML, risk signals, and security implications.
• Ability to collaborate with Engineering and Data Science and discuss technical features.
• Data-driven mindset balancing security with user experience and performance.
• Experience in cloud infrastructure, SaaS, fintech, or complex platforms.
• Strategic prioritization in ambiguous, fast-moving environments.
• Excellent cross-functional communication and stakeholder management.
• Incident response mindset with ability to drive improvements.

Responsibilities

• Own the anti-fraud product roadmap and end-to-end strategy across onboarding, identity, payments, and security.
• Lead fraud detection initiatives covering card fraud, registrations, account takeovers, and abuse.
• Manage risk rules and scoring systems with workflows for analysts and data scientists.
• Improve trust and safety ops with case management tools and investigation workflows.
• Design verification experiences that reduce false positives while protecting users.
• Define risk signals, alerts, thresholds, and detection workflows for early incident detection.
• Coordinate incident response with cross-functional teams and post-incident reviews.
• Translate risk challenges into PRDs and specifications for engineers and data scientists.
• Collaborate on detection models and decide between rule-based vs ML approaches.
• Run experiments, roll out product changes, and monitor detection performance.
• Ensure regulatory and payment compliance with KYC/AML and data privacy standards.
• Track fraud losses, chargebacks, false positives, and review throughput to guide priorities.

Skills

Product management

Analytics

Fraud/risk

Technical product

Data-driven decisions

Cloud/SaaS/fintech

Strategic thinking

Cross-functional

Incident management

Ownership & adaptability

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Product Manager – Anti-Fraud based in Spain.

Join an innovative technology environment building large-scale cloud infrastructure that powers the global AI economy. As Product Manager – Anti-Fraud, you will lead the development of products and systems designed to detect, prevent, and respond to fraud across the entire customer lifecycle. From payment fraud and account takeovers to compute abuse and coordinated exploitation, you will tackle complex challenges that directly affect platform security, operational efficiency, and business sustainability. Working at the intersection of Trust & Safety, Payments, Risk, Data Science, and Engineering, you will turn sophisticated risk problems into scalable product solutions. Your mission will be to protect infrastructure and legitimate customers while keeping unnecessary friction to a minimum. This is an opportunity to take meaningful ownership in a fast-moving, international environment where analytical thinking, technical collaboration, and measurable impact are essential.

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.