Quality Analytics Lead

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Key Responsibilities 1. Quality Data Modeling & Analytics Infrastructure

• Design, build, and maintain dbt models and data marts that serve the Quality organization’s enterprise reporting needs — covering throughput, accuracy, defect rates, CAPA effectiveness, annotator/rater performance, and program-level quality health.
• Use Python for higher-order data modeling tasks including cohort analysis, performance trend modeling, and custom aggregations that go beyond standard SQL/dbt scope.
• Partner with data engineers to define source data requirements, document data lineage, and ensure quality data is reliable, consistent, and analytics-ready.
• Own the quality analytics data layer end-to-end: from raw operational inputs to clean, tested, well-documented marts consumed by dashboards, reports, and ad hoc analyses.
• Apply dbt testing, documentation, and best practices to build a trusted, maintainable codebase that scales as new programs and data sources are onboarded. 2. Quality Measurement Frameworks & Metrics Design
• Collaborate with Quality Managers and Analysts to define, standardize, and operationalize quality metrics — including accuracy rates, defect categorization, sampling coverage, inter-rater agreement, and CAPA closure effectiveness — consistently across all programs.
• Design measurement frameworks aligned to acceptance criteria and quality thresholds, ensuring metrics faithfully reflect program health and client commitments.
• Support rubric and guideline effectiveness measurement, helping quality teams understand whether their standards produce consistent, measurable outcomes across annotators and raters.
• Champion data quality governance within the Quality org: own metric definitions, threshold documentation, and analytical methodology standards to reduce inconsistency and reporting variance.
• Define enterprise-level quality dashboards in partnership with BI resources, translating mart output into clear, decision-ready views for Quality Managers through to senior leadership.
• Analyze patterns in model evaluation outcomes, annotator disagreement, and guideline interpretation to surface systemic issues in AI training data and evaluation processes. 3. Experimental Design & Performance Validation
• Design and execute A/B tests and controlled experiments to measure the impact of quality interventions, process changes, and annotator training programs — applying proper power analysis, significance testing, and results interpretation.
• Build success validation frameworks to confirm that CAPA actions and process improvements produce measurable, sustained outcomes — not just short-term fluctuations.
• Develop performance attribution models that quantify the contribution of specific quality initiatives to outcome improvements, separating causal signal from noise in program performance trends.
• Apply statistical methods to sampling design, audit analysis, and error pattern detection, surfacing systemic quality issues and their root causes with data-backed evidence.
• Conduct pre/post analyses for major quality program changes, training rollouts, and rubric updates, delivering clear impact assessments to quality leadership and clients. 4. Decision Support & Stakeholder Partnership
• Act as the analytical partner to Quality Managers (P2–L2) and senior quality leadership, translating complex data models and analytical findings