Lead conversation-level analytics across AI and human-assisted interactions.
Analyze resolution quality, looping, escalation, and failure patterns across clients.
Identify recurring patterns that reduce customer friction and repeat contacts.
Own DSAT and VOC root-cause analysis linking dissatisfaction to conversations.
Explain how prompts, knowledge sources, playbooks, and tools influence AI outcomes.
Produce decision-grade insights and recommendations to guide AI optimization.

🎯 Requirements

5–8+ years of analytics, decision science, or CX analytics roles.
Strong experience analyzing conversational or interaction-level data.
Hands-on SQL and Python for data exploration and analysis.
Experience with LLMs or GenAI for analysis; writing prompts to extract patterns from data.
Ability to create basic dashboards or visualizations.
Strong understanding of conversation quality, DSAT drivers, and CX metrics.

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