senior data scientist

Business Introduction GSK remains committed to achieving bold commercial ambitions for the future. By 2031, we aim to deliver 40 billion in annual sales, leveraging our existing strong performance momentum to significantly increase our positive impact on the health of billions of patients globally. Our Ahead Together strategy is centred on early intervention to prevent and alter the course of disease, thereby protecting people and supporting healthcare systems.Our diverse portfolio consists of vaccines, specialty medicines, and general medicines.Through continuous innovation and a dedicated focus on scientific and technical excellence, we strive to develop and launch new, groundbreaking treatments that address critical health challenges.Position Summary 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: 1. 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. 2. Define canonical data models, mapping rules, and contextualization patterns across FIL and UNS capabilities for equipment, process, recipe, material, batch, laboratory, and quality data. 3. 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. 4. Convert document-based and scientific knowledge into structured, searchable, and traceable knowledge assets supporting knowledge graphs, retrieval, analytics, and AI-enabled workflows. 5. 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. 6. Apply advanced analytics, machine learning, and graph-based methods to CMC and manufacturing use cases. 7. Establish data quality, metadata, lineage, and observability requirements and partner with Quality, Security, Privacy, and Validation teams to establish controls for regulated use. 8. Provide technical leadership across data scientists, data engineers, integration engineers, architects, CMC subject-matter experts, and strategic partners. 9. Partner with Product Management, Manufacturing Technologies, MSAT, R&D, Quality, Regulatory, and site teams to prioritize use cases and define adoption plans. 10. Represent the capability in architecture and data-governance forums and build internal expertise through coaching and communities of practice. Why You? Basic Qualification We are seeking professionals with the following required skills and qualifications to help us achieve our goals: 1. Bachelor's d gree in Data Science, Computer Science, Bioinformatics, Engineering, Pharmaceutical Sciences, Applied Mathematics, or a related discipline. 2. 6+ years of experience in data science, data modelling, knowledge engineering, industrial data, or advanced analytics. 3. Experience leading complex cross-functional data, analytics, or technology initiatives. 4. Experience delivering production-scale data or knowledge products within pharmaceutical, biotechnology, manufacturing, laboratory, or another regulated environment. 5. Experience designing canonical data models, mappings, ontologies, taxonomies, knowledge graphs, or semantic layers across multiple data sources. 6. Experience integrating or contextualizing manufacturing or laboratory data from systems such as MES, LIMS, ELN, historians, automation systems, or enterprise platforms. 7. 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: 1. Master's degree or PhD in Data Science, Computer Science, Bioinformatics, Engineering, Pharmaceutical Sciences, Applied Mathematics, scientific computing, or a related discipline. 2. Experience using Python and SQL to deliver data science, analytics, graph, semantic, or machine-learning solutions. 3. Experience with Factory Integration Layer (FIL), Unified Namespace (UNS), or similar industrial data integration and contextualization patterns. 4. Experience with semantic technologies and standards such as RDF, OWL, SHACL, SPARQL, or comparable graph and ontology technologies. 5. Experience developing or applying knowledge graphs, enterprise ontologies, controlled vocabularies, taxonomies, or metadata platforms. 6. Experience with streaming, time-series, event-driven, batch, graph, cloud, or scalable data-processing technologies. 7. Experience applying advanced analytics or machine learning within CMC, pharmaceutical development, manufacturing, or laboratory environments. 8. Knowledge of responsible AI, explainability, privacy, security, data integrity, and risk-based validation within regulated environments. 9. Experience working with GxP-regulated data and technology environments. 10. Experience working across scientific, manufacturing, data, engineering, Quality, and technology teams. 11. Experience providing technical leadership, coaching specialists, or establishing standards and reusable technical patterns. 12. Ability to translate complex scientific, manufacturing, and technical concepts for different stakeholder groups. 13. Strong technical writing and communication skills. #GSK-LI #AGTech Skills Cloud Computing, Container Orchestration, Data Center Management, DevOps, D