Role overview
Shaping the future of quality through data, this position leads advanced analytics initiatives that go beyond routine reporting. The Quality Medicine Data Scientist applies techniques such as machine learning, causal inference, artificial intelligence, and natural language processing to identify patterns, generate hypotheses, and support strategic decision-making across global programs. Located at the Regional Center in Vienna, the role collaborates with cross-functional teams to unlock the full potential of real-world data in a regulated and innovation-driven environment.
What you'll work on
- Project ownership in data science: Involves leading initiatives within the Quality Medicine (QM) function to address complex, non-routine analytical challenges related to quality risk and medicinal product lifecycle activities.
- Application of advanced methodologies: Requires the use of machine learning (ML), causal inference, artificial intelligence (AI), natural language processing (NLP), and statistical modeling to extract insights and inform decision-making.
- Development of analytical frameworks: Focuses on creating new hypotheses and models by integrating structured and unstructured data sources, supporting continuous improvement in quality and compliance across Early Clinical Development and Medical Devices.
- Exploration of data-driven opportunities: Entails uncovering hidden patterns, correlations, and risks within large-scale datasets to proactively address quality challenges and enhance operational efficiency.
- Strategic collaboration across functions: Includes acting as a key analytical resource for cross-functional teams, translating complex data into actionable insights and supporting quality-related decision processes.
What we're looking for
- Educational background: Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative discipline.
- Professional experience in analytics: Proven track record in executing full-cycle data science projects—data preparation, model development, validation, and deployment
- Experience in pharmaceutical quality & GxP environments: Demonstrated prior experience working in the pharmaceutical industry, with a solid understanding of Good Clinical Practice (GCP) and the application of data science in regulated quality and compliance contexts.
- Technical expertise in modeling: In-depth knowledge of AI/ML algorithms and causal inference techniques, with demonstrated use in quality and compliance contexts.
- Programming proficiency: Skilled in R, Python or similar languages. Experienced in working collaboratively in git. Familiar with the Posit workbench.
- Cross-functional communication skills: Capable of presenting complex analytical findings clearly and effectively, with experience collaborating across multidisciplinary teams to support strategic initiatives
Please note that employment takes place via one of our external partners. The employment takes place exclusively at Boehringer Ingelheim RCV GmbH & Co KG.
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