The Quality Data Scientist will support AbbVie’s Quality organization by providing data mining, processing/reporting and interpretation expertise. The incumbent will support the process assessments by performing trend monitoring of quality data from the quality control laboratories and manufacturing floor. The individual will support process improvement opportunities that can lead to efficiencies such as reductions in exception events, product testing/release cycle time, capacity planning, and cost reduction, all within the framework of preserving product quality and patient safety. Furthermore, the principal Quality Data Scientist will areas act as a local subject matter expertise in business intelligence reporting applications, and statistical approaches to data analysis and interpretation.
- Identify process improvement opportunities through data science, quality control data trending, and process knowledge.
- Quantify potential savings versus costs, prioritize opportunities, and support implementation.
- Guide teams through DMAIC process, communicate status during project execution, and document results. Author/co-author/review/approve scientific reports and presentations. Report consumers vary but can include quality managers, senior management, internal technical groups, and regulatory agencies.
- Establish commercial data reporting strategies for commercial and pipeline products.
- Anticipate potential sources of quality control issues based on trend monitoring and quality process controls.
- Work with business and IT subject matter experts to build and sustain a data model that makes 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 quality management to automated reports 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.
Bachelor’s degree in a quantitative field such as Data Science, Data Analytics, Statistics, Computer Science, AND/OR applied science field such as Chemistry, Engineering, or Biology.
Minimum 5+ years’ business experience in business analytics, manufacturing data analytics, data mining, or statistical modeling in a cGMP related industry. Manufacturing Biologics life science experience is a must.
Knowledge of global regulatory requirements for pharmaceutical, medical devices, and combination products preferred. Knowledge of FDA Quality Systems, pharmaceutical products, (21 CFR 803, 820 and 211) is preferred.
Demonstrated proficiency with at least one statistical programming package (e.g., R, SAS, iMP, Minitab).
Familiarity in Python, SQL, relational databases (Teradata, Oracle etc.), and/or BI tools (PowerBI, Qliksense).
Experience with data visualization/pipelining tools (e.g., Spotfire, Tableau, Pipeline Pilot etc.) and automated statistical process control platforms (e.g., Discoverant, Statistica, NWA, etc.) is preferred.
Ability to work as part of a diverse team with strong problem solving and interpersonal skills.
Strong communication skills, written and verbal, and strong attention to detail.
Lean Six Sigma Greenbelt certification is preferred.
Significant Work Activities: N/A
Job Type: Experienced