Staff Quality Analyst — Service Quality

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

- 7–10 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

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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.