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About the Role
Own deep analysis of agentic, multi-turn AI conversations to explain customer behavior, identify failure patterns, and influence AI and operational decisions for enterprise customers.
This role focuses on understanding how and why conversations succeed or break, and translating those learnings into actionable insights that reduce customer friction and improve outcomes.
Responsibilities
Lead conversation-level analytics across AI and human-assisted customer interactions.
Analyze resolution quality, looping behavior, escalation effectiveness, and failure patterns across clients.
Identify recurring patterns that help clients reduce customer friction, confusion, and repeat contacts.
Own DSAT and Voice-of-Customer root-cause analysis, linking dissatisfaction to conversation and journey issues.
Explain how prompts, knowledge sources, playbooks, and tools influence AI behavior and outcomes.
Produce decision-grade insights and recommendations that guide AI optimization and product improvements.
Identify systemic patterns across clients, use cases, and channels, not just account-specific issues.
Act as a subject-matter expert for conversational AI analytics within Customer Success and partner teams.
Requirements
6–10+ years of experience in analytics, applied science, decision science, or CX analytics roles
Strong experience analyzing conversational, customer support, or interaction-level data
Hands-on working knowledge of SQL and Python for data exploration and analysis
Experience using LLMs or Gen-AI tools for analysis and insight generation, including writing effective prompts to summarize, cluster, and extract patterns from data
Ability to create basic dashboards or visualizations to communicate findings effectively
Strong understanding of conversation quality, DSAT drivers, and CX performance metrics
Experience working with enterprise-scale customers, platforms, or complex support environments
Proven ability to communicate complex findings in clear, decision-oriented narratives
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