Senior Customer Support Quality Analyst

On the job, you will:

• Drive quality consistency and operational integrity: Review customer interactions to assess compliance, risk, and quality standards, applying strong judgment in ambiguous or edge-case scenarios. Lead and contribute to calibration efforts that strengthen fairness, alignment, and consistency across quality evaluations.
• Deliver behavior-based quality insights: Provide thoughtful, actionable feedback on customer interactions, identifying strengths, opportunities, and recurring behaviors that support coaching, skill development, and performance improvement.
• Investigate patterns and uncover meaningful signals at scale: Leverage AI-assisted tools, QA data, and qualitative analysis to identify recurring behaviors, risks, and opportunity areas across large volumes of customer interactions, surfacing trends that would be difficult to identify through manual review alone.
• Connect insights to root causes and operational improvements: Distinguish between agent behavior, process gaps, and product or tooling issues to identify root causes and translate findings into actionable recommendations for quality processes, workflows, and customer experience improvements.
• Translate insights into coaching and cross-functional action: Synthesize patterns and signals into clear, actionable guidance for Leadership, Enablement, and cross-functional partners, helping drive follow-through and measurable improvements across support operations.
• Contribute to the evolution of the Quality model: Support experimentation, iterative improvement, and new approaches to understanding customer interactions at scale as Mercury continues evolving toward a more insights-driven, AI-assisted Quality function.

You should have:

• 2–4+ years of experience in Quality Assurance or a related Customer Experience role, ideally in a fast-paced, operationally complex, or regulated environment
• Strong quality judgment and systems thinking: You can evaluate how work is done — not just outcomes — and identify patterns, root causes, and opportunities for improvement across people, processes, and tooling
• Insight generation and problem-solving skills: You’re able to investigate open-ended questions using QA data, qualitative examples, and AI-assisted analysis, translating patterns into clear, actionable recommendations that support coaching and operational improvement
• Cross-functional communication and influence: You build trust across teams, communicate insights clearly, surface risks and gaps proactively, and help stakeholders align on actions and move improvement efforts forward through strong follow-through and coordination
• Ownership, adaptability, and continuous improvement mindset: You operate effectively in evolving environments, move ideas into action with minimal guidance, and proactively identify ways to improve systems, workflows, and quality practices over time
• AI-assisted quality tooling experience: Experience using AI-assisted analysis tools (such as Rippit, Claude, or similar platforms) to investigate customer interactions, identify meaningful patterns, and support operational decision-making. Curiosity and experience refining prompts or queries to improve signal quality and reduce noise is highly valued.