Quality Insights Manager, Ring Blink Customer Support Quality Assurance; CSQA team

Position: Quality Insights Manager, Ring Blink Customer Support Quality Assurance (CSQA) team
Description

Ring and Blink Customer Support assists millions of customers annually with product and account questions. Associates resolve inquiries ranging from technical support to account management, delivering consistent and exceptional customer experiences.

The Customer Support Quality Assurance (CSQA) team, as part of the broader Neighbor Excellence organization, provides data-driven insights to elevate associate performance across Ring and Blink customer service interactions. CSQA partners across Operations, Learning, Content, and Technology to implement enhancements that drive customer satisfaction.

We're building next-generation quality analytics and AI-driven measurement systems. To support this transformation, we're seeking a highly technical professional who combines AI/ML knowledge with contact center quality experience to execute analytics and reporting that drive customer satisfaction.

As our Quality Insights Manager, you'll own dashboards, reporting, and analytics that surface trends, themes, and defects in quality performance. You'll leverage AI-enabled tools to accelerate analysis, validate AI model outputs to ensure accuracy, and ensure multilingual parity across evaluations. You'll partner with Data Science and Technology teams to support AI/ML innovation in Quality Monitoring Systems and AI-automated evaluation infrastructure. Your insights enable stakeholders to make data-driven decisions that improve associate quality and customer experience.

Success in this role means stakeholder confidence in data accuracy and reliability, timely delivery of clear insights, consistent AI-driven quality evaluations, and effective cross-functional partnerships that enable stakeholders to execute improvement actions that result in measurable improvement in associate quality results.

Key job responsibilities

Insight Generation & Root Cause Analysis

* Surface trends, themes, and systemic defects to improve associate quality and customer experience

* Translate quality insights into clear, actionable problem statements that enable stakeholders to develop improvement strategies and measurably enhance associate quality and customer satisfaction

* Ensure insights are prioritized based on criteria (risk, scale, and customer impact)

* Maintain clear documentation of insights, assumptions, and confidence levels

Data Accuracy & System Reliability

* Inspect and validate quality data inputs across human and automated evaluations

* Identify and resolve gaps in measurement accuracy, consistency, and reliability

* Ensure multilingual parity in quality evaluation across all supported languages

* Partner with automation and quality teams to improve data integrity

Scalable Analytics & AI Integration

* Own dashboards, reporting views, and analytical frameworks that support proactive quality management

* Use AI tools to accelerate analysis, trend detection, and insight generation

* Support development of predictive models to forecast performance trends and identify emerging risks

* Ensure insight outputs remain clear, explainable, credible, and actionable for non-technical stakeholders

* Support ad-hoc reporting requests for organizational and departmental initiatives, ensuring timely delivery of insights that inform strategic decisions

Leadership Enablement & Risk Management

* Present insights clearly to stakeholders with risk assessment and recommended focus areas

* Escalate appropriately when insights are not acted upon or when data indicates growing customer impact

* Partner with cross-functional teams including Data Science, Product, and Technology to refine evaluation datasets and improve model performance

* Monitor measurement systems and recommend improvements to ensure they evolve with business needs and maintain accuracy at scale

* Monitor reporting mechanism effectiveness and recommend measurement standard improvements based on outcomes, business needs, and stakeholder feedback to maintain relevance and accuracy

A day in the life

You start your day reviewing quality dashboards and data to identify emerging defects, trends, and performance patterns. You pressure-test multilingual evaluation consistency across all supported languages to ensure parity in quality measurement. You support development of predictive models to forecast performance trends and identify emerging risks before they impact customers.

You partner with stakeholders to walk through problem statements and risk areas, ensuring they have clear, actionable insights to drive improvement. When you identify a performance gap, you surface what requires attention, why it matters, and what the customer impact is if left unaddressed - enabling stakeholders to create action plans with clear owners and timelines.

Throughout the day, you work with Data Science and Technology teams to validate AI model outputs, ensuring evaluation accuracy and multilingual consistency. You provide feedback that refines evaluation datasets and improves model performance, supporting measurement systems to…