Define and drive a multi-market Trust & Safety strategy that balances fraud loss reduction with customer conversion—owning the full roadmap from detection accuracy improvements to false-positive reduction and friction minimization.

Lead the end-to-end product lifecycle—from problem discovery through launch and iteration—for fraud detection and prevention systems across all PayJoy markets.

Partner with Data Science and Engineering to ship ML-powered decisioning, AI-driven investigation agents, rule engines, real-time monitoring, and alerting tools that adapt to evolving fraud vectors.

Design frictionless fraud mitigation experiences (identity verification, step-up authentication, device intelligence) that protect users without punishing them.

Identify and prioritize emerging fraud patterns across markets—identity fraud, device manipulation, synthetic identities, AI-generated deepfakes, and agentic attacks—before they scale.

Explore and champion the use of AI and agentic automation to scale fraud operations—automated case triage, adaptive rule tuning, intelligent alert routing—so PayJoy can operate lean fraud ops teams across dozens of markets.

Define and own key Trust & Safety metrics (loss rates, false positive rates, detection latency, customer impact scores) and build dashboards that make fraud posture visible company-wide.

Run experimentation frameworks to measure the real-world tradeoff between fraud prevention tightening and customer conversion impact.

Be the connective tissue between Engineering, Data Science, Fraud Ops, Legal, Compliance, and country GMs—ensuring alignment on fraud tolerance, policy, and customer experience. Communicate risks and roadmap tradeoffs to senior leadership.

6+ years of product management experience, with at least 2–3 years focused on fraud, risk, trust & safety, or payments within fintech or a regulated, high-scale platform.

Proven track record of shipping fraud prevention or risk management products that measurably reduced losses without killing conversion.

Strong technical fluency—you can hold your own in conversations about ML model performance, AI/LLM applications in fraud, API design, data pipelines, and real-time decisioning systems.

Hands-on data skills: you write SQL, build dashboards, and use data to drive every major decision.

Experience driving cross-functional execution with engineering, data science, operations, and compliance teams in a fast-paced, global environment.

Exceptional prioritization instincts—you know how to sequence work across multiple markets and competing urgencies.

Experience with fraud in emerging markets (Latin America, Africa, or Southeast Asia)—where fraud patterns, data availability, and infrastructure look very different from the US/EU.

Familiarity with fraud prevention tools and vendors (e.g., Sift, Arkose Labs, Sardine, or equivalent).

Background in device intelligence, behavioral biometrics, or identity verification technologies.

Prior experience building or integrating ML-based fraud detection models (you don’t need to train them—but you should know how to evaluate and improve them).

Hands-on experience applying AI/LLM agents to fraud operations workflows—automated investigation, intelligent case routing, or generative risk scoring.

Knowledge of compliance regulations related to fraud (e.g., AML, KYC).

Fluency in Spanish.