Data Analyst with AI

Job Description Build and maintain HEART dashboard views with division-level and BU-level drill-downs. Analyze AI adoption across divisions, including active users, new users, repeat users, usage frequency, session depth, task completion, and adoption trends. Create cross-division comparison views for leadership to identify high-adoption groups, low-adoption groups, enablement needs, and AI maturity patterns. Analyze user-created GPT activity, including creation trends, usage frequency, most-used GPTs, underused GPTs, stale GPTs, reclaim candidates, and replication opportunities. Create leaderboard and cohort views for top users by division, top GPT creators, AI Champions, power users, and emerging adoption pockets. Develop AI Champions self-service dashboard views with filters, exports, and role-based reporting by division or BU. Analyze token economics by model, BU, use case, application, provider, and time period. Build spend and consumption reports showing forecast vs. actual spend, cost per user, cost per GPT, cost per use case, token intensity, and quota utilization. Identify abnormal usage patterns, high-cost use cases, model inefficiencies, and opportunities for model rightsizing. Create observability dashboards for RAG and AI infrastructure, including latency, error rates, pipeline freshness, indexing status, retrieval issues, and availability trends. Support dashboard UAT, metric validation, data reconciliation, and stakeholder walkthroughs. Translate analysis into actionable recommendations for AI Ops, AI Champions, finance, and leadership. #J-18808-Ljbffr