• Own the economic framework behind pricing and inventory disposition (contribution margin, holding cost, option value). • Define and evolve the Bleeder / At-Risk / Healthy inventory classification and policies. • Lead structured research and experimentation in ambiguous, low-signal environments. • Partner with data science on time-to-sell, demand, and elasticity models — pushing for interpretability and actionability. • Use LLMs pragmatically to accelerate hypothesis generation, feature ideation, matching, and model evaluation (with guardrails). • Partner with engineering to productionize daily decisioning, guardrails, monitoring, and overrides. • Establish trust through explainable recommendations, governance, and post-mortems. • Apply formal reasoning where useful (constraints, invariants, checkable rationales) to ensure decisions are valid and auditable.