• Analyze large, complex datasets to identify abuse patterns, fraud signals, and harmful behavior trends. • Conduct root cause analysis to diagnose safety incidents and emerging risks. • Evaluate new tool effectiveness (including AI), and impact on agent efficiency and user satisfaction. • Define and track core metrics (e.g., harm prevalence, violation rates, detection accuracy). • Navigate the tradeoff between operational efficiency, safety, and user growth/experience. • Build dashboards and reporting frameworks to track platform health and safety performance. • Develop heuristics, statistical models, and machine learning solutions for proactive detection of abuse, fraud, or harmful content • Build prediction systems (e.g., anomaly detection, risk scoring, behavioral profiling). • Improve automated enforcement and moderation workflows. • Evaluate model performance and iterate on detection strategies. • Design and analyze experiments (A/B tests, causal inference) to measure safety feature impact (e.g., login & verification, AI moderation support). Clearly communicate findings to technical and non‑technical stakeholders. • Quantify tradeoffs between operational efficiency, safety, and user growth/experience. Guide TnS team on key tradeoffs in decision-making • Partner with Product, Engineering, Operations, Policy, and Legal teams to define safety strategy. • Influence decision-making through data storytelling and insights. • Standardize analytical methodologies and tools for scalable decision-making.