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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** Glaxosmithkline PLC - **Location:** Cambridge, MA, United States - **Experience:** Expert - **Salary:** $145,200.0 - $242,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Big Data, Bioinformatics, Information Engineering, Data Governance, Data Integration, Data Integrity, Data Systems, Data Center Infrastructure Management (CIM), DevOps, Digital Technology, Disaster Recovery, Event-Driven Programming, Graph Database, Identity and Access Management, Storage Area Network (SAN), Python (Programming Language), Knowledge Management, Laboratory Information Management Systems, Machine Learning, Automation of Marketing, Metadata, Network Planning and Design, Resource Description Framework (RDF), Zero Trust Network Access, Scientific Computating, Service-Oriented Architecture, SPARQL, SQL Databases, Data Streaming, Systems Integration, Virtualization Technology, Data Processing, Knowledge Engineering, Data Layers, Virtual Computing, Kubernetes, Information Technology, Serverless Computing, GXP - **Published:** October 29, 2026 - **Apply:** https://www.careercircle.com/jobs/all/all/usa/pa/king-of-prussia/1c8e999d-52ed-4fab-9b08-47bcf23806ab ## About the Role Leadership Management Automation Innovation Immunology Ontologies Caregiving Scalability Time Series Data Quality Data Science Communication Data Analysis Observability Data Modeling Biotechnology Data Integrity Responsible AI Pharmaceuticals Decision Making Data Governance Data Processing Knowledge Graph Computer Science Data Integration Machine Learning Data Engineering Technical Acumen Technical Writing Disaster Recovery Virtual Computing Analytical Method IT Risk Management Advanced Analytics Product Management Digital Technology Early Intervention Automation Systems Workflow Management Applied Mathematics Infectious Diseases Knowledge Management Technical Leadership Architectural Design Scientific Computing Serverless Computing Controlled Vocabulary Knowledge Engineering Information Technology, * Bachelor's degree in Data Science, Computer Science, Bioinformatics, Engineering, Pharmaceutical Sciences, Applied Mathematics, or a related discipline. * 6+ years of experience in data science, data modelling, knowledge engineering, industrial data, or advanced analytics. * Experience leading complex cross-functional data, analytics, or technology initiatives. * Experience delivering production-scale data or knowledge products within pharmaceutical, biotechnology, manufacturing, laboratory, or another regulated environment. * Experience designing canonical data models, mappings, ontologies, taxonomies, knowledge graphs, or semantic layers across multiple data sources. * Experience integrating or contextualizing manufacturing or laboratory data from systems such as MES, LIMS, ELN, historians, automation systems, or enterprise platforms. * Experience establishing data quality, metadata, lineage, testing, or operational controls for data solutions. Preferred Qualification If you have the following characteristics, it would be a plus: * Master's degree or PhD in Data Science, Computer Science, Bioinformatics, Engineering, Pharmaceutical Sciences, Applied Mathematics, scientific computing, or a related discipline. * Experience using Python and SQL to deliver data science, analytics, graph, semantic, or machine-learning solutions. * Experience with Factory Integration Layer (FIL), Unified Namespace (UNS), or similar industrial data integration and contextualization patterns. * Experience with semantic technologies and standards such as RDF, OWL, SHACL, SPARQL, or comparable graph and ontology technologies. * Experience developing or applying knowledge graphs, enterprise ontologies, controlled vocabularies, taxonomies, or metadata platforms. * Experience with streaming, time-series, event-driven, batch, graph, cloud, or scalable data-processing technologies. * Experience applying advanced analytics or machine learning within CMC, pharmaceutical development, manufacturing, or laboratory environments. * Knowledge of responsible AI, explainability, privacy, security, data integrity, and risk-based validation within regulated environments. * Experience working with GxP-regulated data and technology environments. * Experience working across scientific, manufacturing, data, engineering, Quality, and technology teams. * Experience providing technical leadership, coaching specialists, or establishing standards and reusable technical patterns. * Ability to translate complex scientific, manufacturing, and technical concepts for different stakeholder groups. * Strong technical writing and communication skills . #GSK-LI #AGTech, Cloud Computing, Container Orchestration, Data Center Management, DevOps, Disaster Recovery (DR), Identity Access Management (IAM), Information Technology (IT) Risk Management, Integration Platform as a Service (iPaaS), Network Design, Serverless Platforms, Service Oriented Architecture (SOA), Storage Area Network (SAN), System Monitors, Technical Architecture, Technical Architecture Design, Virtualization, Zero Trust Architecture ## Description Event-Driven Programming Authorization (Computing) SQL (Programming Language) Storage Area Network (SAN) Network Planning And Design General Scientific Knowledge Python (Programming Language) Service-Oriented Architecture Medical History Documentation Identity And Access Management Resource Description Framework (RDF) Laboratory Information Management Systems Integration Platform As A Service (IPaaS) SPARQL Protocol And RDF Query Language (SPARQL), The Senior Data Scientist will create connected data and knowledge capabilities that enable CMC Acceleration and modern digital manufacturing. This role will transform fragmented scientific, manufacturing, laboratory, and quality information into governed, contextualized, and reusable data products that support faster decision-making, scalable analytics, artificial intelligence, and digital technology transfer. This role operates at the intersection of data science, semantic modelling, manufacturing integration, and knowledge management. The role will help shape data patterns across Factory Integration Layer (FIL), Unified Namespace (UNS), mapping and contextualization services, enterprise ontologies, CMC knowledge assets, and data-harness capabilities. The role combines hands-on technical expertise with leadership across complex, cross-functional initiatives, establishing reusable and technology-agnostic standards and partnering with data scientists, engineers, architects, CMC specialists, and delivery partners. Responsibilities This role will provide YOU the opportunity to lead key activities to progress YOUR career. These responsibilities include some of the following: * Lead the design and delivery of data science and knowledge-engineering solutions for CMC Acceleration, translating scientific and manufacturing requirements into governed data products and semantic models. * Define canonical data models, mapping rules, and contextualization patterns across FIL and UNS capabilities for equipment, process, recipe, material, batch, laboratory, and quality data. * Develop and govern CMC ontologies, controlled vocabularies, taxonomies, and semantic relationships across products, processes, unit operations, equipment, analytical methods, materials, specifications, sites, and manufacturing capabilities. * Convert document-based and scientific knowledge into structured, searchable, and traceable knowledge assets supporting knowledge graphs, retrieval, analytics, and AI-enabled workflows. * Shape data-harness capabilities that ingest, contextualize, and expose information from manufacturing, laboratory, and enterprise sources, including MES, LIMS, ELN, historians, automation platforms, and external partners. * Apply advanced analytics, machine learning, and graph-based methods to CMC and manufacturing use cases. * Establish data quality, metadata, lineage, and observability requirements and partner with Quality, Security, Privacy, and Validation teams to establish controls for regulated use. * Provide technical leadership across data scientists, data engineers, integration engineers, architects, CMC subject-matter experts, and strategic partners. * Partner with Product Management, Manufacturing Technologies, MSAT, R&D, Quality, Regulatory, and site teams to prioritize use cases and define adoption plans. * Represent the capability in architecture and data-governance forums and build internal expertise through coaching and communities of practice.