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 operational and executive audiences

Communicate uncomfortable findings credibly: when the data contradicts the prevailing narrative, make the case with rigor and land it with senior stakeholders

5. AI-Native Analytics

Use modern AI tools — LLM-based assistants and agentic workflows — as a core part of the analytical workflow, accelerating defect detection, root cause analysis, and prioritization from weeks to days

Design and iterate AI-assisted workflows across the defect reduction loop, from automated signal identification to outcome tracking and reporting

Build repeatable AI-assisted playbooks for defect identification and root cause analysis that the whole team can run — making the analytical craft scalable rather than dependent on individuals

Stay current on emerging AI capabilities relevant to quality analytics and proactively apply them to customer outcomes

Qualifications

Required

Strong hands-on analytical skills: SQL proficiency and working fluency in Python or R for analysis of large operational datasets; solid grounding in applied statistics (distributions, hypothesis testing, control charts, confidence in what a metric movement does and does not prove)

Demonstrated experience turning operational data into prioritized, quantified improvement roadmaps — and validating whether the improvements worked

Experience with defect- or rate-based measurement frameworks (DPMO or equivalent) and translating process-level defects into outcome-level impact

Experience in complex, high-stakes service operations — contact centers, fintech, financial services, or expert/advisor networks

Demonstrated ability to build dashboards and analytical products that drive leadership decisions, including with AI-powered tools

Working knowledge of structured improvement methods (Lean, Six Sigma, root cause analysis) and experience using them to drive fixes from analytical findings

Experience driving accountability in matrixed organizations without direct authority over execution teams

Bachelor's degree in a quantitative field (Data Science, Statistics, Industrial Engineering, Operations Research, Economics, Business Analytics) or equivalent professional experience in operational analytics

Preferred

Lean Six Sigma certification (Green or Black Belt) — valued as a tools credential, applied through an analytical lens

Experience with causal inference or experimentation methods (A/B testing, difference-in-differences, treatment-effect estimation)

Experience with Conversation Intelligence, speech/text analytics, or AI-based quality measurement systems (conversation scoring, resolution scoring)

Knowledge of back-office work management concepts — case management, work item tracking, SLA design outside live-interaction contexts

High-technology or software industry background

Change management training (Prosci/ADKAR, Kotter, or equivalent)

Experience That Will Set You Up for Success

7+ years in operational analytics, quality analytics, or continuous improvement roles where the deliverable was a measurable business outcome, not a report

Owning a metric or defect domain end-to-end: instrumentation, analysis, initiative prioritization, and outcome validation — with the experience of explaining results both when the needle moved and when it did not

Working with analytics and data science teams as a peer — including designing or materially influencing the measurement frameworks you rely on

Presenting contested findings to senior stakeholders and facilitating alignment when the status quo has defenders

Driving change in dynamic environments where the operating model is evolving and business-as-usual inertia is real

What Success Looks Like

Success in this role is measured by outcome movement — not analysis volume, not initiative count. You will know you are succeeding when:

The defects in your domain are declining as a measured rate, and you can demonstrate — with defensible analysis — the connection between that decline and movement in customer resolution, quality, or friction outcomes

Your defect backlog is prioritized by quantified customer impact, and the prioritization survives challenge

The quality outcome for your domain is predictable, because the leading indicators are instrumented and the interventions are statistically proven

Cross-functional partners accept your analysis as the shared basis for accountability

The analytical playbooks and measurement disciplines you build outlast any single initiative — the team's capability grows because of your work

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: San Diego $151,000 - $204,000