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 opera