Operations Quality - Principal Quality AI & Enterprise Data Strategist

Role Purpose

As the Operations Quality Principal Architect specializing in Quality AI & Enterprise Data Strategy, you will be the visionary technical and strategic driver boosting our AI journey and establishing a sustainable foundation within the Insights & Innovations Subchapter.

This role is designed for an advanced data architect and AI strategist who can design safe, scalable, production-ready AI models and automated solutions. Operating at an enterprise level, you will serve as a strategic translator-bridging the gap between cutting-edge data science and rigorous quality governance. You will architect robust, risk-based, GxP-compliant data products that systematically shift our global organization from reactive metrics to predictive excellence.

Key Responsibilities

Enterprise AI Architecture, Scaling & Strategy

Enterprise AI Platform Management: Design, scale, and deploy robust machine learning and NLP pipelines utilizing approved enterprise data platforms, primarily focusing on Dataiku and Azure ML Studio layers.

Agentic Framework Implementation: Evaluate and deploy advanced AI solutions. Utilize the Claude Agent SDK or comparable approved SDKs as targeted implementation options for complex use cases that genuinely require custom, automated agents to navigate cross-system data silos.

AI Use-Case Backlog Governance: Architect and mature our global AI Use-Case Backlog. Guide the deployment of Natural Language Processing (NLP) and Large Language Models (LLMs) to automate initial sorting, risk-grading, and real-time categorization of complex quality data.

Sustainable Data Infrastructure: Oversee the conceptual data modeling and integration pipelines feeding into core analytics repositories (such as Snowflake and AWS). Partner with builders to translate complex datasets into high-performing enterprise business intelligence views.

Strategic Product Ownership & Automated Governance

Agile Product Architecture: Assume formal Product Owner and architectural design oversight for core Quality Insight platforms, dashboards, and automation frameworks.

DevOps & Workflow Optimization: Establish and Champion clean repository management, version control, and collaborative code review practices via Git. Drive standardized automation workflows utilizing Jira and Confluence to optimize the subchapter's intake pipeline and eliminate communication latency.

GxP Compliance, CSV Integration & Matrix Leadership

Computerized System Validation (CSV) Alignment: Ensure all deployed AI models, automated scripts, and data pipelines adhere to computerized system validation protocols under GxP, US FDA (21 CFR Part 820), and ISO 13485 standards. Guide the team in authoring, reviewing, and executing technical validation documentation for data integrity.

Cross-Functional Strategy: Partner directly with Quality Site Heads, Business Leaders, and global cross-functional squads to break down organizational data silos and resolve systemic reporting barriers across site networks.

Community of Practice: Establish, champion, and mentor an Operations Quality Community of Practice for Quality AI & Data Insights. Elevate organizational digital fluency by coaching other team members on best practices.

Technical Performance Indicators (KPIs)

Foundation Sustainability: Successful deployment and architecture of a centralized, enterprise-approved AI platform layer that scales across multiple global use cases.

Accuracy & Compliance: Maintain a 95%+ alignment between automated AI/analytical insights and manual audit findings, fully meeting software validation protocols.

Speed-to-Insight: Drastically reduce the cycle time required to compile and generate monthly and quarterly quality reports across the global network via automated data assembly.

Adoption: Secure high active usage metrics for all owned enterprise data products, capturing measurable efficiency savings.

What Is In Your Toolbox (Qualifications & Experience)

Required Skill Set & Competencies:

AI & Machine Learning Architecture: Strong hands-on engineering proficiency with enterprise data science environments (such as Dataiku or ML Studio). Advanced understanding of NLP, LLMs, and prompt engineering architectures.

Developer & Execution Tooling: Strong proficiency in core programming for data data modeling (Python, SQL) and developer workflows (Git, Jira, Confluence, and script automation environments).

Agentic Framework Familiarity: Hands-on experience or deep conceptual understanding of custom agent architectures utilizing tools like the Claude Agent SDK or comparable enterprise-approved development kits.

Data Systems Awareness: Solid structural knowledge of cloud data warehousing (Snowflake), pipeline concepts (AWS), and business intelligence delivery tools (ThoughtSpot, Tableau).

Background & Experience

Education: Bachelor's, Master's, or Ph.D. degree in Data Science, Informatics, Data Analytics, Computer Science, or a related technical field.

Professional Experience: 5-7+ years of experience building and deploying machine learning algorithms, complex data models, or automated software tools to support enterprise-level decision-making.

Domain & Validation Knowledge: Experience working within a highly regulated manufacturing environment (such as Pharma, Diagnostics, or Medical Devices). Sound understanding of GxP compliance, US FDA 21 CFR Part 820 Quality Management System Regulations, and ISO 13485/9001 standards. Prior experience with Computerized System Validation (CSV) protocols is highly preferred.

Strategic Matrix Leadership: Proven capability acting as a principal technical leader, driving global cross-functional projects, and aligning stakeholder expectations with a team-first, agile mindset.

Locations & Travel

You are based in your designated site location. As this position is part of a global organization, international business travel will be required depending upon ongoing project and strategic activities