Product Manager – Specialist II (Decision Engines & Anti-Fraud)

New

JobgetherFraud prevention

Based in BrazilFull-TimeMiddle

Salary not disclosed

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Job Details

Experience managing the full lifecycle of complex digital products, particularly transactional platforms, technology infrastructure, payments, credit, or risk and fraud prevention.
Hands-on experience leading legacy modernization, including monolithic-system decoupling, microservices architectures, messaging, and API integrations.
Ability to coordinate transitions between legacy and modern environments while maintaining business continuity and minimizing disruption.
Strong stakeholder management, negotiation, and relationship-building skills, including influencing without formal authority.
Ability to communicate and translate Engineering and Data Science discussions into insights for non-technical decision-makers.
Analytical, data-driven decision-making skills and familiarity with conversion-versus-risk, transactional churn, false positives, and chargebacks.
Ability to structure ambiguous problems, identify uncertainties, and make strategic decisions with incomplete information.
Academic background or professional experience in Computer Science, Engineering, Economics, or a related field.
Experience in anti-fraud environments, particularly rule engines and real-time integration of statistical models and machine learning solutions.
Experience with large-scale omnichannel ecosystems in Retail, Franchising, E-commerce, or high-volume Fintech is a plus.

Define the strategic vision, tactical roadmap, and delivery milestones for the anti-fraud and decision-making platform.
Lead legacy-system modernization and migration toward a microservices-based, event-driven architecture.
Build relationships with Business, Fraud Prevention Operations, Credit, Commercial Channels, and Compliance teams.
Translate technical and architectural concepts into business value and align technical priorities with organizational objectives.
Establish product governance and communicate architectural decisions, risks, dependencies, and progress to senior stakeholders.
Define, monitor, and optimize product and operational indicators, including fraud losses, false positives, analysis latency, risk engine SLAs, and system stability.
Partner with Tech Leads, Software Architecture, Engineering, and Data Science to integrate predictive machine learning models and business rules.
Ensure platform capabilities support high availability, operational efficiency, and real-time decision-making.
Promote continuous discovery, structured hypotheses, MVP definition, and technical refinement of API contracts.
Balance transaction security, fraud prevention, operational performance, and customer experience throughout the product lifecycle.

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