Staff Quality Analyst — Service Quality

About the Organization Expert Network Operational Excellence owns service quality outcomes across Intuit's Expert Network — the thousands of tax, bookkeeping, and financial experts who serve our customers every day. The organization brings together Continuous Improvement, Business Systems, Automation, and our Quality Management platform into a single team accountable for driving down customer experience defects and building the capabilities to sustain quality at scale. The Service Quality team sits at the heart of this organization. We are the outcome owners — responsible for identifying defects in the customer experience, quantifying them, prioritizing them against measurable outcomes, and driving the initiatives that eliminate them. We operate through a modern, AI-powered measurement architecture anchored to three customer-facing outcome pillars: • Resolution: Are we fully resolving what customers contact us about? Measured by AI-based scoring across every customer contact, from the customer's perspective. • Interaction quality: Are we meeting the quality standard in every interaction? AI-scored across ~100% of customer interactions — not a sampled QA program. • Customer friction: Are we finding and fixing the friction — repeat contacts, transfers, delays — that accumulates into poor customer experiences? Every defect this team works earns its place on the roadmap by demonstrating a measurable, quantified connection to one of these outcomes. We prioritize by customer impact — proven with data, not asserted. Role Overview The Staff Quality Analyst is a senior individual contributor responsible for the analytical engine of defect reduction across the Expert Network. You own the measurement system that decides what gets fixed: you design defect detection and quantification approaches, build and analyze defect funnels, isolate root causes in large operational datasets, size the customer impact of candidate fixes, and validate — statistically, not anecdotally — that improvement initiatives actually moved resolution, quality, and customer friction outcomes. This is an analytics role with a quality mission. You will apply descriptive, diagnostic, and causal analysis to one of the richest operational datasets anywhere — AI-scored conversation data covering ~100% of customer interactions — and translate it into a prioritized, defensible improvement roadmap. Structured improvement methods (Lean, Six Sigma, Kaizen, root cause analysis) are tools in your kit for driving the fixes your analysis identifies; the analysis itself is the craft. You will operate as a single-threaded owner for specific defect domains and hold cross-functional partners — service delivery, training, partner management, product, and platform teams — accountable for their share of the quality outcome using evidence they cannot argue with. Responsibilities 1. Analytics Strategy & Defect Measurement • Design the measurement approach for assigned defect domains: define the defect, the opportunity base, and the DPMO (Defects Per Million Opportunities) instrumentation so every defect rate ladders directly to a customer outcome metric • Build and maintain defect funnels — quantifying defect volume, severity, and contribution to outcome degradation — and evolve the defect taxonomy as the operating environment changes (new categories, refined severity weights, expanded coverage) • Prioritize the defect backlog analytically: rank initiatives by modeled customer impact, and defend the ranking with data when it is contested • Contribute to the continuous improvement of the quality measurement architecture itself — ensuring we measure the right things, validating that our measures stay true, and pressure-testing quality markers against outcome data 2. Analysis, Root Cause & Outcome Validation • Analyze resolution, quality, sentiment, and customer friction signals across large operational datasets (SQL and Python/R against conversation-level and journey-level data) to identify defects and isolate root causes — moving from symptom to verified cause, not plausible narrative • Design the leading-indicator instrumentation beneath each outcome metric and monitor it for signal, drift, and regression • Validate initiative impact with appropriate rigor: pre/post analysis with controls, cohort comparison, or causal methods where the stakes demand them — and say clearly when the needle did not move and why • Partner with analytics and data science teams to evaluate, adopt, and improve AI-native quality measurement (conversation scoring, resolution scoring, customer distress detection) — including validating AI-scored metrics against human-labeled ground truth 3. From Insight to Improvement — Driving the Fix • Own the operating loop for assigned defect domains: defect detected → root cause verified → initiative launched → outcome validated → loop closed • Deploy structured improvement methods — root cause analysis, Kaizen and rapid improvement events, value stream mapping, standard work — as the delivery mechanism for what the analysis has prioritized • Translate analytical findings into technical solutions and roadmap commitments in partnership with systems and platform teams • Drive adoption and change management for the fixes, and embed the measurement disciplines (daily management, leader standard work) that keep improvements from decaying • Hold dependencies — product, platform, routing, back-office — accountable for their share of customer friction using quantified evidence and structured escalation 4. Data Visualization, Insights & Executive Communication • Build and maintain the dashboards and reporting that surface defect signals, track outcome metrics, and support leadership decisions — designed for action, not decoration • Present defect reduction progress and outcome movement in leadership operating mechanisms (daily huddles; weekly, monthly, and quarterly business reviews) with clear data storytelling for both operationa