• Own the Trust & Safety product strategy and roadmap translating business risk and user insight into a clear, prioritised plan. • Shift the function from reactive incident response to proactive, intelligence-driven fraud prevention: build the Risk Engine, data signals intelligence, anomaly detection, and investigation tooling that make this possible. • Ship AI-native T&S product experiences: partner with Data Science and Engineering to design, prototype, and productionise real-time detection and decisioning systems using ML models, LLMs, and behavioural signals. • Balance fraud mitigation with growth protection—ensuring T&S interventions do not unnecessarily create friction for legitimate users or throttle marketplace supply/demand. Make hard, evidence-based trade-off decisions. • Define and track the right T&S success metrics: fraud loss rates, false positive/negative rates, time-to-resolution, user trust scores, and financial exposure. Communicate impact in business terms to senior leadership. • Partner closely with Engineering leads, Data Science, Legal, Compliance, and Operations—holding shared accountability for outcomes and navigating competing priorities. • Build long-term instrumentation, monitoring, and evaluation capabilities to measure fraud rates, detection model performance, and contributor/user impact at scale. • Establish business cases quantifying fraud losses and ROI of prevention investments; secure resources and drive prioritisation grounded in data. • Ensure products and strategies meet local and regional compliance, legal, and security requirements across all markets. • Apply AI to how you operate as a PM: faster threat analysis, synthetic user research on abuse patterns, AI-assisted spec writing, automated experimentation pipelines—whatever moves the needle on velocity. • Represent Trust & Safety at the leadership level, presenting strategy, risk posture, and results clearly to executive audiences.

• Own the vision and roadmap for the policy development and testing tools, execution engine, data sources connector and content management interfaces • Run customer development with internal users (risk analysts, risk engineers, domain product teams) yourself - from interviews to validated problem statements • Do the full analysis cycle hands-on: requirements and business analysis, problem decomposition, solution options with trade-offs - leveraging the team and LLM tools where they help, including digging into the codebase • Drive the self-service target state and be responsible for the platform's key metrics: lead time for content delivery, self-service coverage, SLA, user CSAT, etc. • Prioritize one backlog across Risk B2C, Risk B2B, product streams - by quantified impact • Work daily with engineering on architecture trade-offs • Prototype hands-on, including with LLMs - validate ideas before consuming engineering time; design and automate internal processes • Own platform OKRs and answer for them at C-level

• Own the mobile product roadmap from discovery through launch and iteration • Prioritize initiatives based on customer needs, business impact, and technical feasibility • Define product requirements and collaborate closely with Design and Engineering • Analyze user behavior, product metrics, and customer feedback to identify growth opportunities • Drive experimentation, feature launches, and continuous product improvements • Partner with Operations, Growth, and Customer Success teams to improve the user journey • Define and monitor key product KPIs, including activation, engagement, retention, and conversion • Champion data-driven, customer-centric product decisions

• Own the mobile product roadmap from discovery through launch and iteration • Prioritize initiatives based on customer needs, business impact, and technical feasibility • Define product requirements and collaborate closely with Design and Engineering • Analyze user behavior, product metrics, and customer feedback to identify growth opportunities • Drive experimentation, feature launches, and continuous product improvements • Partner with Operations, Growth, and Customer Success teams to improve the user journey • Define and monitor key product KPIs, including activation, engagement, retention, and conversion • Champion data-driven, customer-centric product decisions