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