About the job
As a Risk Strategy Product Manager, you are the architect of the end-to-end risk defense lifecycle. This role demands a "full-stack" mindset, owning the journey from problem definition to impact validation. You will leverage data-driven insights and AI technologies to build scalable, automated risk frameworks. Serving as a global strategic node, you will collaborate with regional teams, business partners, and technical stakeholders to protect the ecosystem from evolving adversarial threats.
Responsibilities
- End-to-End Domain Ownership: Lead the strategic roadmap for specific global risk domains. Design and implement systemic defense frameworks—including risk engine integration, feature logic, and decision pathways—ensuring a proactive defense posture.
- AI & Agent Orchestration: Drive the transformation of risk operations through AI. Translate complex risk judgment criteria into LLM prompts and agentic workflows to automate decision-making, replacing manual processes with scalable AI-driven solutions.
- Advanced Data Attribution: Conduct deep-dive root cause analysis (RCA) on strategy performance and model health. Interpret feature distributions and data trends to identify drifts, and autonomously adjust thresholds to balance risk levels with business growth.
- Global Scalability & Implementation: Translate complex risk logic into standardized global playbooks and quality benchmarks (QA) to ensure consistent defense performance across all localized markets.
- Technical Bridge & Execution: Translate complex risk requirements into scalable product roadmaps and technical specifications, driving cross-functional execution across Data, Algorithm, Engineering, and Operations teams.
Qualifications
Minimum
+5 years of experience in Risk Management, Trust & Safety, or Anti-Fraud within large-scale technology ecosystems, with a proven track record of effectively detecting and mitigating high-concurrency adversarial attacks.
Proficiency in SQL with the ability to autonomously extract insights and risk signals from large-scale, complex datasets.
Strong understanding of machine learning principles, specifically in designing high-impact features and evaluating model performance (e.g., Precision, Recall, AUC) to optimize risk-reward trade-offs.
Hands-on experience with AI/LLM applications, including Prompt Engineering and the orchestration of AI agents to solve operational challenges.
Exceptional ability to influence and collaborate across diverse teams, including Business, Data, Engineering, and Operations.
A self-starter with a strong sense of ownership. Comfortable making high-stakes, data-backed decisions in ambiguous environments.
Preferred
No preferred qualifications listed.