Director, Quality Analytics

Vertex Inc.•Boston, MA

1d•Hybrid

About The Position

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. This role is a people manager, responsible for building and developing a high‑performing team, establishing clear priorities and operating rhythms, and ensuring analytics and AI solutions are implemented in a controlled, compliant manner suitable for regulated environments. The role partners cross‑functionally to improve data quality and reliability, expand predictive and NLP capabilities, and enable fit-for-purpose AI and agentic solutions using approved enterprise platforms. This individual will demonstrate Vertex Core Values, as well as model leadership behaviors instilling a culture of quality, engagement, development, and transparency.

Requirements

Demonstrated ability to turn data into information that helps drive decisions.
Highly skilled in 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
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

Nice To Haves

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

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.