Data Scientist - Cambridge, MA

 

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You’ve worked hard to become the professional you are today and are now ready to take the next step in your career. How will you put your skills, experience and passion to work toward your goals? At Amgen, our shared mission—to serve patients—drives all that we do. It is key to our becoming one of the world’s leading biotechnology companies, reaching over 10 million patients worldwide. Come do your best work alongside other innovative, driven professionals in this meaningful role.

Data Scientist


What you will do

Let’s do this. Let’s change the world. In this vital role you will be part of the ASDP group providing support to Amgen’s commercial portfolio through application of process monitoring, data engineering, data analysis through statistical and machine learning algorithms, and engineering principles. Responsibilities will include identifying abnormal data trends in product and method data, identification of root causes for trends and collaborating cross-functionally to implement corrections. In addition, this role will focus on identifying and deploying optimization opportunities. The position offers the opportunity to engage with multiple product modalities across a global manufacturing and quality control network.

Apply statistical trending tools to product and method data Apply engineering principles and multivariate statistical analysis in-order to understand sources of variation and evaluate opportunities for improvements
Use data analytics software to develop visualizations to review, assess, and improve analytical method performance
Interface with analytical scientists, process development scientists, and engineers to assess, understand, and support data consumption needs
Interface with product quality, QC, quality assurance, and manufacturing organizations to resolve method issues
Support ongoing GMP activities such as the generation of the annual product review documents, regulatory inspections, and process documentation
Stays abreast of new technologies and statistical approaches to recommend improvement