Process Data Scientist

Job SummaryThe Data Scientist will work with process Development and Assay Development scientists, Process Engineers, Quality Control Analysts and other cross-functional teams to understand and analyze data from Manufacturing and Quality Control processes. Serving as the statistics lead on Manufacturing Science and Technology projects employing various data analysis techniques and statistical methodologies such as regression analysis (both linear and non-linear), cluster analysis, CHAID, factor analysis, etc. Ensure the timeliness, quality and statistical validity of all analyses, output, and presentations.Work with large and complex data sets to solve a wide-range of challenging problems using different analytical and statistical approachesProvide statistical and data interpretation support to the wider MS&T function as well as overseeing and contributing to the overall data strategy and infrastructure support underpinning CRL’s CDMO CGT programAssisting in the implementation of data harvesting, cleansing, and analysisActs as the subject matter expert regarding all statistical and data analytic needs within the MS&T function in support of the wider data analytical frameworkResponsible for designing and implementing data harvesting, cleansing and analysis strategies and in conjunction with key partners such as IT to ensure deployment of the most effective and compliant infrastructureResponsible for ensuring the level of training amongst the wider MS&T function is knowledgeable of the required activities with regard to statistical and data analytical techniques.Job QualificationsBachelor's degree in statistics, economics, mathematics, life sciences, engineering, or relevant courseworkAdvanced degree (Masters, PhD) preferredMinimum 5 years working in the pharmaceutical industry in technical and/or process statistical roleMastery of various statistical methodologies such as regression analysis (both linear and non-linear), cluster analysis, CHAID, factor analysis, principal component analysis, etc.Excellent verbal and written communication skillsAchievement in the pharmaceutical manufacturing environment preferredDemonstrated proficiency in scientific creativity, collaboration with others and independent thought in suggesting experimental design to support and/or lead process development, and support objectivesProven ability to identify and implement data analytical enterprise solutions in support of investigation, routine trending, and Continued Process Verification.

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