CX Quality Assurance Lead

Responsibilities

• Own a defined area of the QA program end-to-end (calibration and rubric governance, AI-powered analytics, or partner quality) and drive it from signal to action
• Set quality standards and rubric logic for your area; ensure consistent application across channels and partners and recommend framework updates based on outcomes
• Configure and continuously improve AI-QA tooling (ObserveAI or similar) with set scoring logic, monitoring accuracy, and ensuring the platform delivers consistent, actionable output
• Build automation into QA workflows to reduce manual overhead and expand coverage; establish AI-assisted processes for status updates, reporting, and initiative recaps
• Analyze quality and conversation data to surface DSAT drivers, behavioral trends, escalation patterns, and workflow gaps by going beyond what happened to understand why
• Design and maintain dashboards that track key quality metrics; translate findings into clear operational recommendations for CX leadership
• Produce structured insight reports (clear signal, root cause, recommended action) and ensure findings flow into training, content, coaching, and product decisions
• Contribute QA input to WBR/MBR cycles and cross-functional planning; give leadership consistent visibility into program health and customer experience impact
• Partner with BPO teams and CX stakeholders to align on standards, drive calibration, and keep expectations consistent across every channel and partner
• Collaborate with Enablement, Knowledge, and Programs to ensure quality signals drive decisions across training, content, and coaching and deliver initiatives that measurably improve agent quality and customer satisfaction

Requirements

• 4–7 years in CX operations, QA, or analytics — you've genuinely owned a program (not just contributed to one) and have specific, measurable outcomes to point to
• Comfortable pulling and analyzing data without a dedicated analyst (Looker, Metabase, Hex, or similar; SQL is a plus)
• You've worked with QA and conversation intelligence tools (ObserveAI, Rippit, Level AI, Klaus, or similar) and know how to configure, calibrate, and get meaningful output from them
• You use AI as part of how you actually work with automations, workflow improvements, faster paths to insight
• You've produced insight reports that moved real decisions, where training shifted, processes changed, policy updated
• You don't just identify problems, you design the processes that prevent them from recurring
• You can translate quality data for non-QA audiences and influence decisions without formal authority
• You are comfortable in ambiguity and move fast without sacrificing rigor, and you're fine building the playbook as you go
• You are fluent in quality evaluation methodologies, calibration processes, and QA framework design
• You have a track record coordinating across operations, training, content, and technology teams to deliver shared goals
• You are a strong communicator, demonstrated with clear status reports, insight briefs, and stakeholder presentations