Senior Specialist, Data Science

About the Organization The Biologics Science and Technology Platforms, Data, Modeling, and Statistics (PDSM) organization is a highly technical, science-forward function embedded within Bio S&T. We partner closely with manufacturing sites, IT, and process engineers to deliver data-driven process insights, statistical modeling, and digital capabilities that accelerate biologics commercialization and manufacturing excellence. Our mission is to bridge the gap between traditional process engineering and modern data science. We build the foundational architectures, digital workflows, and analytical models that underpin every initiative across the biologics network—enabling proactive process monitoring (PPM), continued process verification (CPV), yield optimization, tech transfer, and AI-ready manufacturing. We work hand-in-hand with our IT and manufacturing partners to co-design process analytics platforms. Our team contributes deep bioprocessing domain understanding paired with technical data science capabilities, ensuring the right process parameters and quality attributes are captured, contextualized, and modeled to drive real operational outcomes.

Summary The Senior Specialist, Advanced Process Analytics & Data Strategy is a technical individual contributor role within PDSM. The primary expectation is hands-on execution and support, applying data science, process modeling, and data architecture concepts to strengthen biologics manufacturing analytics. The successful candidate will use their bioprocess engineering background to develop, maintain, and improve analytics solutions, support process standardization, and collaborate closely with scientists, engineers, and digital teams. We are seeking candidates who fit the Domain-to-Data Professional profile: A bioprocess, biochemical, or regulated manufacturing engineer who has developed meaningful data science expertise through hands-on work with process, analytical, and batch data. You must be able to apply tools such as Python, R, SQL, and statistical modeling to support process characterization, digital analytics, root-cause investigations, and regulatory-ready manufacturing data products.

1. Biologics Process Analytics & Engineering Support Support the development and execution of process analytics activities that connect unit operations, process parameters, and quality attributes through structured manufacturing data models. Translate bioprocessing and manufacturing needs into clear data requirements and help convert available data capabilities into practical scientific and operational insights. Contribute to process-focused data initiatives by performing analysis, developing datasets and visualizations, documenting requirements, and coordinating with engineers, scientists, and IT partners. 2. Manufacturing Data Architecture & Contextualization Build and maintain a clear data flow map across the biologics manufacturing network, integrating core manufacturing systems (MES, LIMS, PI Historian, SAP, ELN). Support process data contextualization and ontology mapping by helping link raw process and analytical data across unit operations, sites, and product lifecycle stages. Collaborate with IT and data engineering partners to support scalable, GxP-compliant data solutions by providing bioprocess domain context, data validation, and user requirements. Support data integrity expectations by applying ALCOA+ principles during data review, validation, documentation, and routine use of manufacturing data products. 3. Process Monitoring, Modeling & Statistical Enablement Develop and deploy fit-for-purpose dashboards, process visualizations, and analytics to enable Proactive Process Monitoring (PPM), trend identification, and rapid root-cause investigation support.Work with Statistical Sciences and Process/Product Modeling teams to prepare, structure, and validate datasets that support CPV, digital twins, AI/ML models, and multivariate