Staff Quality Analyst — Quality Management System

We need to output HTML fragment with sections: Overview? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). Do NOT repeat the job title as a heading. We need to include sections only when posting has real specific content. The posting includes Overview (which is like About the role). Then Responsibilities (multiple subparts). Then Qualifications (Required, Preferred). Then Experience That Will Set You Up for Success (maybe part of Qualifications? Could be separate but likely part of Qualifications/Requirements). Then compensation/pay info: Intuit provides a competitive compensation package... Expected Base Pay Range For This Position Is San Diego $102,000 - $138,000. So we have Pay section. No explicit Benefits or Schedule. So we should not create those sections unless present. So sections: About the role (Overview), Responsibilities, Qualifications (maybe split Required and Preferred? Could combine but we can have subheadings? The instruction only says use

for section headings; we can have nested? Probably just top-level sections. Could have Qualifications with subpoints. Could also have Experience That Will Set You Up for Success as part of Qualifications. Could also have a separate section "Experience" but not listed in examples; but we could treat as part of Qualifications. Safer to include Qualifications section covering Required, Preferred, and Experience That Will Set You Up for Success. Pay section. We need to use

• for any list of duties/requirements/benefits. So for Responsibilities we have multiple bullet-like paragraphs; we should convert each responsibility bullet into
• . The responsibilities are grouped under headings: Analytics Strategy & Defect Measurement, Analysis, Root Cause & Outcome Validation, From Insight to Improvement — Driving the Fix, Data Visualization, Insights & Executive Communication, AI-Native Analytics. Those could be subheadings but not required; we could just list all responsibilities as list items. However we need to preserve ALL substantive information; we can keep subheadings as

  ? Not allowed? Only

  for section headings. Could use

  for subheadings but not specified; but we can just keep them as plain text within

  maybe with bold? Safer to keep as

  with the heading text as part of the item. Similarly for Qualifications: Required and Preferred bullet lists. We'll create

  • for each. Experience That Will Set You Up for Success: also bullet list. Pay: just a

    with the pay range. We must not include boilerplate like EEO, navigation, etc. Remove application instructions etc. The posting includes "Intuit provides a competitive compensation package..." and pay range. We'll keep that. We must not repeat job title as heading. So no

    Staff Quality Analyst. We'll output something like:

    About the role

    Overview The Staff Quality Analyst is a senior individual contributor ... (the overview paragraph). We need to preserve all substantive information from Overview. The overview paragraph is long; we can keep as

    . Then

    Responsibilities

    • 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... etc.
    ... etc. We need to ensure we capture all bullet points under each subheading. Let's extract responsibilities text. From posting: Responsibilities Analytics Strategy & DefectMeasurementDesign 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 metricBuild 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 contestedContribute 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 Analysis, Root Cause & Outcome ValidationAnalyze 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 narrativeDesign the leading-indicator instrumentation beneath each outcome metric and monitor it for signal, drift, and regressionValidate 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 whyPartner 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 truthDesign and operate human in the loop (HITL) quality processes: calibration sessions, human review sampling, and ground truth labeling workflows that keep AI scoring aligned to human judgment, with clear accuracy gates before any measure goes live From Insight to Improvement — Driving the FixOwn the operating loop for assigned defect domains: defect detected → root cause verified → initiative launched → outcome validated → loop closedDeploy 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 prioritizedTranslate analytical findings into technical solutions and roadmap commitments in partnership with systems and platform teamsDrive adoption and change management for the fixes, and embed the measurement disciplines (daily management, leader standard work) that keep improvements from decayingHold dependencies — product, platform, routing, back-office — accountable for their share of customer friction using quantified evidence and structured escalation Data Visualization, Insights & Executive CommunicationBuild and maintain the dashboards and reporting that surface defect signals, track outcome metrics, and support leadership decisions — designed for action, not decorationPresent 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 audiencesCommunicate uncomfortable findings credibly: when the data contradicts the prevailing narrative, make the case with rigor and land it with senior stakeholdersOwn the communications that accompany measurement change: release notes, rebaseline narratives, FAQs, and stakeholder briefings that explain what changed, why it changed, and what it means for the numbers leaders watchApply structured change management to metric rollouts and platform changes, sequencing communication, training, and adoption support so changes land without disruption or misinterpretation AI-Native AnalyticsUse 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 daysDesign and iterate AI-assisted workflows across the defect reduction loop, from automated signal identification to outcome tracking and reportingBuild 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 individualsPractice responsible AI: ensure AI scoring is explainable, monitored for bias and drift, validated against human judgment, and governed with clear human oversight and escalation paths when automated measurement gets it wrongStay current on emerging AI capabilities relevant to quality analytics and proactively apply them to customer outcomes We need to split into list items; each subheading could be its own list item with the detailed description. Now Qualifications: 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 workedExperience with defect- or rate-based measurement frameworks (DPMO or equivalent) and translating process-level defects into outcome-level impactExperience working in, or strong working familiarity with, the contact center environment, including scaled service operations, BPO or vendor partner delivery models, and the metrics and operating cadences of contact center workStrong communication and change management skills, with demonstrated experience landing measurement or process changes across large stakeholder groups without disruption or misinterpretationDemonstrated ability to build dashboards and analytical products that drive leadership decisions, including with AI-powered toolsWorking knowledge of structured improvement methods (Lean, Six Sigma, root cause analysis) and experience using them to drive fixes from analytical findingsExperience driving accountability in matrixed organizations without direct authority over execution teamsBachelor's degree in a quantitative field (Data Science, Statistics, Industrial Engineering, Operations Research, Economics, Business Analytics) or equivalent professional experience in operational analytics Preferred 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 contextsFintech or financial services industry experience is a plusExperience with human in the loop (HITL) evaluation programs, calibration, or ground truth labeling operations for AI scored measurementFamiliarity with responsible AI principles, including explainability, bias monitoring, and human oversight of automated decision systemsHigh-technology or software industry backgroundChange 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 reportOwning 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 notWorking with analytics and data science teams as a peer — including designing or materially influencing the measurement frameworks you rely onPresenting contested findings to senior stakeholders and facilitating alignment when the status quo has defendersDriving change in dynamic environments where the operating model is evolving and business-as-usual inertia is real We need to split Required, Preferred, and Experience That Will Set You Up for Success. We could have

    Qualifications

    then maybe sub-sections? But instruction only says use

    for section headings; we could still have

    inside but not required. Safer to just have one Qualifications section and within it we can have

    • for each bullet, but we need to differentiate Required vs Preferred vs Experience. Could embed headings inside
    • like Required: then list? But that might be considered filler? Probably okay. However we could also create separate sections:

      Required Qualifications

      ,

      Preferred Qualifications

      ,

      Experience That Will Set You Up for Success

      . The instruction says use

      for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It lists examples but not exhaustive; we can use other headings as long as they are

      . So we can create those. Thus:

      About the role

      Overview ...

      Responsibilities

      • 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... (full text)
      • 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... (full text)
      • From Insight to Improvement — Driving the Fix: Own the operating loop