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Job Responsibilities:

Key Responsibilities

1. Quality Data Modeling & Analytics Infrastructure

• Design build and maintain dbt models and data marts that serve the Quality organizations 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 (P2L2) and senior quality leadership translating complex data models and analytical findings into clear actionable insights for program decisions.
• Produce client-ready analytical deliverables including quality performance summaries trend analyses and post-mortem reports that Quality Managers can present in client governance reviews and executive forums.
• Proactively monitor quality performance data to identify emerging risks and flag issues to quality leadership before they escalate into client-impacting problems.
• Lead discovery conversations with quality stakeholders to understand their data needs translate them into well-scoped analytical requirements and ensure delivered solutions address the actual decision being made.
• Coach quality team members on data-driven decision making helping them frame analytical questions interpret results and design measurement into their processes from the start.

5. Roadmap Ownership & Continuous Improvement

• Maintain and prioritize a backlog of analytics projects in support of the Quality organizations evolving needs balancing quick-turn analyses with longer-term data infrastructure investments.
• Identify and implement opportunities to automate recurring quality reporting and analysis reducing manual effort for quality teams and improving consistency and timeliness.
• Maintain and update a backlog/roadmap spanning multiple workstreams regularly communicating progress blockers and trade-offs to Analytics and Quality leadership.
• Stay current on emerging best practices in quality analytics experimental design and AI evaluation methodology recommending new approaches where they would meaningfully improve outcomes.
• As this function matures lay the groundwork for a dedicated Quality Analytics capability: document processes build reusable frameworks and onboard any future team members.

Qualifications & Experience

Education

Bachelors degree or equivalent work experience in Computer Science Data Science Statistics Engineering or a related quantitative field. Preferred: post-graduate education or equivalent professional experience in analytics data modeling or data engineering.

Required Experience

• 5 years in a data analytics analytics engineering or data modeling role with demonstrated ownership of analytical data products in a production environment.
• Proven experience designing and building dbt models including mart architecture testing documentation and version-controlled development workflows.
• Strong Python proficiency for data analysis and modeling (e.g. pandas numpy statsmodels or equivalent).
• Advanced SQL skills for complex analytical queries data exploration and data quality validation.
• Hands-on experience designing and analyzing controlled experiments or A/B tests including statistical significance testing power analysis and practical results interpretation.
• Demonstrated ability to translate business requirements from non-technical operational stakeholders into well-scoped analytical solutions.

Preferred Experience

• Exposure to quality operations AI training data workflows annotation platforms or BPO/localization environments.
• Familiarity with QA frameworks sampling methodology CAPA processes rubric design or quality management systems in a data-intensive context.
• Experience working in an embedded analytics role supporting an operational team with accountability for both analytical outputs and the underlying data infrastructure.
• Proficiency with BI tools Power BI preferred for delivering analytical outputs to non-technical stakeholders.
• Familiarity with ELT/pipeline tooling (e.g. Matillion Fivetran or equivalent) and how data flows from operational systems into analytics-ready layers.

Technical Skills

• Required: dbt (models marts tests documentation) Python (data analysis and modeling) SQL (advanced) Git/version control.
• Preferred: Power BI or equivalent BI platform ELT pipeline tooling statistical modeling libraries (Python) familiarity with data warehouse environments (e.g. Snowflake BigQuery or similar)

Core Competencies

• Technical rigor with operational empathy: the ability to deeply understand quality teams day-to-day challenges and translate them into well-designed purposeful analytical solutions not over-engineered abstractions.
• Strong analytical and statistical reasoning including applied experience with experimental design performance attribution and hypothesis testing in messy real-world operational data.
• Exceptional communication: able to translate complex data models and analytical findings into plain-language insights for quality managers senior leadership and clients across a diverse range of technical literacy levels.
• Self-directed and proactive: comfortable managing a diverse project backlog with competing priorities delivering consistently without close supervision and raising blockers early and clearly.
• Collaborative and intellectually curious: genuinely interested in understanding quality processes and domain context deeply enough to ask the right questions before building.
• Growth orientation: excited about building a new function from the ground up and committed to documenting scaling and sharing work in a way that creates lasting organizational value.

Additional Job Details:

What Success Looks Like

In the first year a successful Lead Quality Analytics Specialist will have made a measurable difference to how the Quality organization uses data. Broadly success in this role means:

• Quality teams treat the data layer as a single source of truth metric definitions are standardized across programs and there is no ambiguity about how key quality indicators are calculated or sourced.
• Quality managers can detect systemic issues earlier: anomalies error pattern drift and sampling gaps surface through data before they become client-impacting problems.
• Quality interventions are measurable CAPA actions training rollouts and process changes have a clear analytical validation framework so outcomes can be confirmed not assumed.
• Manual reporting burden is significantly reduced: recurring quality reports and data extracts that were previously assembled by hand are automated freeing quality teams to focus on analysis and action rather than data preparation.
• Analytics and Quality leadership have a shared view of program performance and the Quality organization can point to data-driven decisions that improved outcomes for clients.