CAPA Trending Data Analyst

Found in: Resume Library US A2 - 2 weeks ago

North Chicago Illinois, United States AbbVie Full time
Job Description

Come to work each day with an inclusive and collaborative business technology team. As a CAPA Trending Data Analyst in AbbVie Business Technology Solutions (BTS), you’ll have opportunities to contribute to the digital transformation of a leading biopharma company, helping to create solutions that impact patients and their communities for the better.

This position can be remote anywhere in the U.S.

Our Business Technology Solutions (BTS) team shapes the digital transformation accelerating the future of medicine at AbbVie—and we do it together, asking bold questions and taking on tough challenges through deep, honest collaboration. In the role of CAPA Trending Data Analyst, you will join a diverse, global team that invests in your customized career growth and uses technology and data to positively impact people’s lives.

The CAPA Trending Data Analyst will guide AbbVie’s global IT quality organization by providing process knowledge and statistical expertise to drive data-driven decision making. The incumbent will utilize data science methodology (e.g., machine learning, AI, advanced analytics, etc.) to determine the impact of IT incidents  on applications, processes and products, and perform trend monitoring of IT quality data. The individual will engage in process improvement opportunities that can lead to efficiencies such as reductions of cycle time, and cost reduction, all within the framework of preserving product quality. The Senior Data Analyst will also drive the approaches and technology used for trend monitoring and data science initiatives in technical and quality areas.

Responsibilities:

·        Identify process improvement opportunities through data science, machine learning, manufacturing site engagement, and applied global supply chain and process knowledge. Quantify potential savings versus costs, prioritize opportunities, and champion implementation.

·        Perform root cause analysis in support of complaint and process investigations. Guide teams through DMAIC process, communicate status during project execution, and document results.

·        Author/co-author/review/approve scientific reports and presentations to support and document process deviations/investigations and recommendations for corrective actions, determine impact in product quality, and support regulatory submissions. Report consumers vary but can include quality managers, senior management, internal technical groups, and regulatory agencies.

·        Establish commercial data footprints for commercial and pipeline products. Anticipate potential sources of manufacturing and post market issues based on FMEAs, supply chain variability, and process controls. Ensure critical data is available and linkable across geographically diverse manufacturing networks.

·        Perform ongoing monitoring and baseline activities to ensure that product quality trending complies with internal procedures and regulatory expectations.

·        Manage process performance metrics and actions/alerts generated from trend monitoring systems.

·        Work closely with product performance teams (PPTs) and BTS to build automated reporting systems that efficiently translate raw data into consumable information. Serve as a bridge between these groups to translate business needs into IT language and to help stakeholders leverage emerging information technologies.

·        Provide statistical and software training for relevant personnel across the organization as the need arises to improve awareness and understanding of data analysis offerings and techniques.

·        Create and manage ETL procedures and lead data discovery processes for data model creation and multidimensional models. Manage data model connections for automated data pulls and dashboard updates. Ensure that data models will meet the requirements of analytics and visualization tools to inform business decisions.

·        Create automated dashboards that meet business requirements and deliver value across the organization using data from data models spanning multiple systems and databases.

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