Director, Quality Analytics

Role Summary

The Director, Quality Analytics is accountable for advancing the Quality Analytics Center of Excellence by delivering trusted, decision-grade insights across Quality and GxP domains. This role leads a team that designs and scales metrics, dashboards, and advanced analytics to strengthen quality governance, operational oversight, and continuous improvement across modalities and product lifecycle stages. As a people manager, they build and develop a high-performing team, set clear priorities and operating rhythms, and ensure analytics and AI solutions are implemented in a controlled, compliant manner suitable for regulated environments. The role collaborates cross-functionally to improve data quality and reliability, expand predictive and NLP capabilities, and enable fit-for-purpose AI using approved enterprise platforms.

Responsibilities

• Partner with Quality and business leadership to implement integrated analytics and governance that provide timely insight into GxP compliance, process performance, risk signals, and resource utilization.
• Establish and maintain a scalable Quality metrics framework (KPI library/definitions, calculation logic, refresh cadence, ownership, and reporting standards) to enable consistent management oversight.
• Lead prioritization and demand management for analytics work (intake, triage, roadmap, delivery, sustainment), balancing strategic initiatives with operational needs.
• Lead development of governed dashboards, scorecards, and semantic models that enable self-service insights while maintaining appropriate controls.
• Oversee integration of multi-source datasets and automation of recurring reporting to reduce manual effort and increase speed-to-insight.
• Lead application of data science methods (e.g., statistical modeling, forecasting, anomaly detection, trend detection, process capability, risk scoring) to proactively identify issues and improvement opportunities.
• Partner with stakeholders to operationalize analytics products (model + pipeline + monitoring) and measure sustained impact.
• Establish and scale NLP capabilities to extract structured insights from unstructured Quality content (e.g., narratives, observations, notes, free-text fields) to support trending, classification, summarization, and knowledge discovery.
• Identify, evaluate, and deliver prioritized LLM-enabled use cases for Quality using approved enterprise platforms.
• Partner with enterprise teams to enable scalable, governed datasets and pipelines in modern data platforms such as Snowflake and/or enterprise data environments, ensuring secure access and appropriate governance.
• Promote “right-first-time” data practices by collaborating with process/system owners to improve data standards, completeness, and consistency for critical Quality data.
• Develop team capability across BI/analytics engineering, data science, NLP, and GenAI/agentic AI and ensure appropriate training and skill progression.
• Build, lead, and develop a high-performing team, set clear goals, priorities, and expectations.
• Drive adoption of analytics through structured change management execution.

Skills

• Demonstrated ability to turn data into information that helps drive decisions.
• Highly skilled in data modeling, leveraging analytical languages and tools (e.g. Python, R, SAS, SQL, Alteryx)
• Strong data visualization skills and experience with relevant tools (e.g. Spotfire, Power BI, and/or Tableau).
• Experience applying NLP methods to unstructured text (classification, extraction, summarization, search) to generate insights.
• Experience with modern data platforms such as Snowflake and/or enterprise data ecosystems, ability to partner effectively with data engineering teams.
• Strong understanding of data governance and data quality concepts (definitions/standards, lineage, monitoring, stewardship collaboration).
• Experience designing or deploying LLM-enabled solutions using enterprise platforms
• Experience with utilizing scripting languages to leverage APIs (REST APIs etc.,)
• Strong oral, written and presentation skills with ability to explain complex concepts clearly to a variety of audiences
• Understanding of industry requirements/expectations of a Quality Management System
• Current knowledge of industry trends and best practices for progressive quality management in a regulated environment
• Creative, innovative leadership experience complemented with strong change management
• Experience, adaptability, resourcefulness
• Strong problem solving and critical thinking skills, accompanied by analytical thinking/data analysis skills required to make sound decisions
• Experience developing and executing adoption plans that include communications, stakeholder engagement, and readiness activities

Education

• PhD or Master's in a data-related field with a substantial engineering, business and statistical component
• 10+ years’ experience working in pharmaceutical / biotechnology / healthcare / health IT industry, and 3+ delivering end-to-end data analysis projects or the equivalent combination of education and experience
• Demonstrable experience in the pharmaceutical, biotechnology or device industry solving business and/or compliance challenges through the application of analytic approaches strongly preferred.
• Experience with Veeva QMS and other eQMS systems is preferred