Data-Driven Quality Innovation Intern

2 days ago


North Chicago, Illinois, United States AbbVie Full time
The Quality Knowledge Management Automation Intern position at AbbVie offers a dynamic and challenging environment for individuals who are passionate about using data-driven insights to drive innovation and improve patient outcomes. As a key member of the PSEQ organization, you will work closely with colleagues across the product lifecycle to ensure quality standards and protect patients worldwide.

This role is an excellent fit for individuals who are interested in developing new skills, contributing to the development of cutting-edge medical devices, and making a significant impact on AbbVie's product development process. You will have the opportunity to work with a diverse team of experts, develop new skills, and contribute to the development of innovative solutions that improve patient outcomes.

The Quality Knowledge Management Automation Intern will support the Medical Device Combo Product Research and Development Quality Assurance (MDCP RDQA) organization, which focuses on developing and launching new medical device products to patients globally. You will work closely with the team to identify, evaluate, understand, and communicate the safety profile of AbbVie products.

As a Quality Knowledge Management Automation Intern, you will lead efforts to build tools, such as AI BOTs, to collect and organize existing SOPs, regulations, standards, guidance documents, and existing knowledge to facilitate easy search and promote consistent application across R&D Medical Device Combo Product group members. You will also explore potential ways to automate quality activities, such as Design History Files (DHF) documentation reviews to catch/correct basic documentation issues.

Key responsibilities include supporting a team of R&D QA development partners around the globe, leading efforts to build tools to collect and organize existing quality documentation procedures, standards, guidance documents, and existing knowledge, and exploring potential ways to automate quality activities, such as DHF documentation reviews.

To be successful in this role, you should have a strong foundation in computer science, data analytics, or a related field, and experience working with data banks, data structures, and data analysis. Familiarity with machine learning concepts and techniques is also highly desirable. Additionally, proficiency in data mining and extraction techniques, as well as understanding of data governance and data quality principles, are essential for this position.

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