• Lead complex, multi-signal investigations (e.g., account takeover, automation, fraud rings, API abuse) and produce clear, actionable remediation plans. • Serve as the CX technical authority in escalations, guiding hypothesis formation, analysis strategy, and root-cause conclusions. • Translate customer context (risk tolerance, user base, operational constraints, compliance) into measurable detection and tuning objectives. • Identify emerging fraud patterns and cross-tenant trends; conduct quarterly intelligence briefs for customers and recommended mitigations. • Design and recommend detection strategies combining rules, features, and (where applicable) model-driven scoring to reduce fraud while controlling false-positive costs. • Develop reusable feature definitions and tuning approaches that can be applied across customers (not only one-off tenant fixes). • Partner with engineering and products to shape product roadmap priorities, improve explainability and investigation tooling (e.g., “why did this fire,” attribution, drill-down paths). • Define and own CX measurement systems: detection quality, false-positive impact, operational metrics, customer outcomes, and performance over time. • Lead offline evaluation and backtesting methodologies for fraud controls and customer configuration changes. • Drive experimentation practices (A/B where feasible, quasi-experimental, pre/post with controls) and ensure results are decision-grade. • Detect data drift and performance degradation signals; propose retraining triggers and/or mitigation plans in partnership with ML/Engineering. • Create “investigation packs” for R&D: evidence, root cause hypothesis, recommended technical changes, and measurable acceptance criteria. • Mentor and develop other DS/analysts on CX through playbooks, code reviews, investigation standards. • Partner with GTM teams to quantify customer value, communicate outcomes, and support renewals/expansions through data-backed narratives.