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Staff Quality Analyst — Service Quality

Intuit · Posted Oct 14

Financial, accounting, tax preparation, and marketing automation software services

Bangalore, IndiaFull-timeOnsiteLead/Staff7–10 years₹45.0L–₹65.0L yearly100+ applicants
Financial TechnologyAccountingTax PreparationSoftwareSaaSPublic Company
Full time

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About the role

The Expert Network at Intuit focuses on service quality across customer interactions, using AI-scored conversation data covering approximately 100% of interactions. The Staff Quality Analyst is a senior individual contributor who owns defect measurement and analysis, builds defect funnels, isolates root causes, and validates whether improvement initiatives improve resolution, quality, and customer friction outcomes. The role partners with service delivery, training, partner management, product, and platform teams to drive a prioritized defect-reduction roadmap.

What you will do

  • 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
  • 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
  • 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
  • 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
  • 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

Skills used in this role

What the employer is looking for

  • 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
  • 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

Preferred qualifications

  • 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)

Benefits and support

  • cash bonus
  • equity rewards

About Intuit

Intuit logo

Intuit

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Intuit is a leading global financial technology platform that provides software solutions including TurboTax, QuickBooks, Credit Karma, and Mailchimp. The company serves consumers, small businesses, and accounting professionals with tools for tax preparation, financial management, payroll, credit monitoring, and marketing. Intuit focuses on leveraging artificial intelligence and automated workflows to fuel financial success for tens of millions of customers worldwide.

Industry
Financial Technology
Company size
10001+ employees
Founded
1983
Location
Mountain View, California, USA
Funding stage
Public Company

Leadership

SG
Sasan Goodarzi

Chairman & Chief Executive Officer

SA
Sandeep Aujla

Executive Vice President & Chief Financial Officer

AB
Alex Balazs

Executive Vice President & Chief Technology Officer

AS
Ashley Still

Executive Vice President & General Manager, Small Business & Mid-Market Group

MN
Mark Notarainni

Executive Vice President & General Manager, Consumer Group

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