Lonza in Porriño, Spain is looking for an experienced Data Scientist to join our Manufacturing Science and Technology (MSAT) team.As our Data Scientist, you will be responsible for developing real-time process monitoring tools and analytics used for fault detection as well as prediction.This individual will be required to execute the efficient exploration and interpretation of complex data sets by constructing statistical models to summarize and explain process variability and equipment anomalies. He or she will interface daily with Process Experts to mine, clean and explore data sets used to identify causal factors as well as potential opportunities to predict ideal process outcomes, optimize timing of critical events, process productivity, product quality, and general process knowledge.The role will be required to identify and apply appropriate modeling or statistical techniques suitable to unique problem statements.Key responsibilities :Owner locally of the Lonza Global Database and associated systems, and their maintenance in Lonza Porriño.Participate in the investigation of both basic and complex process events and anomaly investigation.Collaboration with Global teams in strategic projects and implementation of global policies in Lonza Porriño.Develop automated predictive process monitoring tools and systems.Data analysis via standard methods, e.g. Regression Analysis, Clustering Analysis, Dimensionality Reduction Analysis.Perform Data mining, cleaning and data-set prep in order to execute complex multivariate statistical analysis (MVA).Develop and apply AI and Machine Learning (ML) to applications of data mining, prescriptive analytics, process monitoring.Works with Technical teams to support data collection, virtualization and integration.Be proactive and bring initiatives to the team, both for the quality of the analysis and for finding efficiencies on a day-to-day basis.Key requirements :Masters of Science degree in Statistics or Data Science required. The ideal candidate will also have a background in Biology and Bioprocess Engineering.Demonstrated understanding of related industrial or bio-pharmaceutical manufacturing processes, data analysis via standard methods (e.g. Regression Analysis, Clustering Analysis, Dimensionality Reduction Analysis) with ML / AI expertise.Experience in manipulating / managing complex data sets, Oracle and SQL database querying / manipulations, Multi-Variant Analysis testing and optimization, Extract Transform Load tools.Excellent written and verbal communications skills to document relevant findings and investigational process in clear structured manner are required.Natural sciences background a plus.C, C++, SAS, MatLab, Minitab, SPSS, Statistica, PowerBI, SeeQ, SVB, T-SQL. #J-18808-Ljbffr
