Quality AI/ML Engineer I

What Will You Do?

The Quality ML/AI Engineer is a highly technical and analytical role supporting the Quality organization and broader enterprise initiatives through the design, development, and deployment of advanced data, machine learning, and artificial intelligence solutions. This role focuses on building scalable and compliant analytical frameworks that connect Quality and business systems to enable proactive product monitoring, advanced quality metrics, improved decision-making, and operational efficiencies.

This position is responsible for developing and maintaining code-based analytical solutions, integrating data across GxP and non-GxP systems, and applying statistical, machine learning, and AI techniques to regulated data sets to support compliance, post-market surveillance, product performance monitoring, and continuous improvement initiatives. The role partners closely with Quality, Global Technology Services, Manufacturing/Operations, R&D, and cross-functional business stakeholders to ensure solutions are technically robust, scalable, and aligned with regulatory expectations.

How Will You Make an Impact?

Data Engineering, Architecture & Integration

• Design, develop, and maintain scalable data pipelines and integrations connecting Quality, Manufacturing, Post-Market, and enterprise systems such as QMS, ERP, PLM, CRM, complaint handling, and adverse event platforms.
• Develop and maintain production-ready analytical code using tools such as Python, SQL, R, or similar programming languages to support ETL/ELT processes and advanced analytics.
• Build scalable data models, repositories, and analytical frameworks that support cross-platform insights while maintaining data integrity, traceability, and compliance in regulated environments.
• Ensure data solutions align with data governance standards, computerized system compliance requirements, and validated system expectations.

Machine Learning & Artificial Intelligence Development

• Develop, validate, and maintain machine learning and AI models to support quality monitoring, operational efficiency, predictive analytics, and business insights.
• Apply statistical, machine learning, and AI methodologies based on business needs, data maturity, risk considerations, and intended use.
• Partner with Quality, Operations, and Regulatory stakeholders to define model intent, limitations, and appropriate application within Quality Management Systems and business operations.
• Build explainable, transparent, and scalable models suitable for regulated and non-regulated decision-support environments.
• Contribute to enterprise-wide ML/AI initiatives that drive innovation, automation, and continuous improvement across the organization.

Quality Metrics, Monitoring & Reporting

• Design and automate dashboards, reporting tools, and monitoring solutions using code-based and business intelligence platforms to provide near real-time visibility into product and system performance.
• Translate complex analytical outputs into clear, actionable insights for leadership, Management Review activities, and cross-functional teams.
• Continuously assess data quality, model effectiveness, and reporting relevance, refining analytical approaches as business and regulatory needs evolve.

Compliance, Validation & Risk-Based Assurance

• Ensure analytical tools, ML models, and AI solutions are developed and maintained in compliance with applicable Medical Device, Pharmaceutical, and Combination Product regulations.
• Support risk-based validation, change management, and documentation activities for analytical systems in accordance with internal procedures and regulatory expectations.
• Maintain documentation related to model design, assumptions, data sources, limitations, and ongoing performance monitoring appropriate for GxP environments.

Collaboration & Continuous Improvement

• Collaborate cross-functionally to translate business challenges into data-driven and AI-enabled solutions.
• Support investigations, audits, inspections, and regulatory interactions related to data integrity, quality metrics, and analytical systems.
• Identify opportunities to improve efficiency, increase automation, and reduce manual reporting efforts across the Quality organization and broader enterprise.
• Contribute to a collaborative, innovative, and continuous improvement-focused culture.

How Will You Get Here?

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Life Sciences, Chemistry, or a related technical field.
• 1-2 years of experience in data science, analytics, machine learning, software-enabled analytics, or related technical roles, preferably within a regulated GxP environment.
• Hands-on experience developing and maintaining analytical or production code using tools such as Python, SQL, R, or similar programming languages.
• Experience building reports, analyzing data, and solving business problems through data-driven insights and automation.
• Exposure to machine learning and/or AI model development for business, operational, or quality-related applications.
• Experience integrating data across multiple enterprise systems and structured or unstructured data sources.
• Understanding of Medical Device, Pharmaceutical, and/or Combination Product quality systems, regulatory expectations, and computerized system assurance concepts.
• Knowledge of data integrity principles, validation approaches, and risk-based methodologies in regulated environments.
• Strong analytical, problem-solving, communication, documentation, and stakeholder collaboration skills, with the ability to work effectively in fast-paced cross-functional environments.
• Experience working with validated quality software, along with proficiency in Microsoft Office applications (Excel, Word, PowerPoint, Visio), and a self-motivated, detail-oriented approach with flexibility to support evolving business needs.