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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Scientist, Data Science - **Company:** J&J Family of Companies - **Location:** Madrid, Spain - **Salary:** €56,000 - €88,780 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Data Analysis, Code Review, Continuous Integration, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Stores, Data Visualization, Graph Database, Industry Standard Architecture, Python (Programming Language), Laboratory Information Management Systems, Machine Learning, Meta-Data Management, Neo4j, NoSQL, Software Tools, Cloud Services, SAP (Applications), SPARQL, SQL Databases, Data Streaming, Systems Integration, Unstructured Data, Workflow Management Systems, Cloud Platform System, Data Ingestion, Snowflake, Data Layers, Information Technology, Health Level Seven International, Software Version Control, Data Pipelines - **Published:** November 4, 2026 - **Apply:** https://dejobs.org/x/x/8B1053C5BED141F8AE4EFCBE7152FD61/job/ ## About the Role · Advanced degree in Engineering, Data Science, Life Sciences, Computer Science, or a related field. · 1-3 years of experience in data product engineering, data integration, or data pipeline development. · Proficiency in Python and SQL, and familiarity with cloud-based data platforms, such as AWS or Snowflake. · Understanding of data modeling, ETL/ELT patterns, and data quality principles. · Experience with or exposure to knowledge graphs, graph databases, or RDF triple stores, using platforms such as Neo4j, Amazon Neptune, or GraphDB; familiarity with representing and querying entities and relationships. Experience with all listed platforms is not required. · Strong problem-solving skills and ability to work across multiple concurrent projects. Preferred · Experience with NoSQL databases. · Familiarity with scientific, manufacturing, or healthcare data environments. · Exposure to regulated or standards-driven data environments (e.g., CDISC, HL7, ISA-88/95). · Experience with workflow orchestration tools. · Practical experience applying semantic models, controlled vocabularies, or ontologies to data integration and reusable data products. · Exposure to embedding generation, vector indexing, and retrieval workflows for unstructured scientific or technical content. · Familiarity with graph query languages such as Cypher or SPARQL., Advanced Analytics, Business Intelligence (BI), Coaching, Collaboration, Critical Thinking, Data Analysis, Database Management, Data Privacy Standards, Data Reporting, Data Savvy, Data Science, Data Visualization, Econometric Models, Process Improvements, Technical Credibility, Technologically Savvy, Workflow Analysis ## Description The R&D Data Science organization is seeking a Senior Scientist, Data Science with a focus on data product engineering to build and maintain scalable data pipelines, data integration workflows, and AI-ready data products that support analytics, AI/ML applications, and digital transformation across the development-to-supply continuum for Therapeutics Development & Supply (TDS). You will be a hands-on technical contributor working across Process Development, Manufacturing, Supply Chain, Quality, Digital and Data Science teams to deliver high-quality data pipelines and reusable data products that support scientific, technical, and operational decision-making. The role includes developing data products that preserve scientific and operational context through metadata, semantic models, and relationships between entities, and preparing unstructured content for AI applications through embedding generation and vector indexing. You will work with domain experts, data architects, and data scientists to implement these capabilities within established enterprise standards and technical architectures., Data Pipeline Development & Maintenance · Build and maintain data pipelines for integrating, cleaning, and transforming TDS data from diverse sources and systems (ELNs, MES, LIMS, SAP, quality systems, clinical supply platforms) into reusable and governed data products · Develop and optimize data flows for structured and unstructured data using Python, SQL, cloud services, and modern engineering tools. · Support the creation and maintenance of TDS-specific data repositories, implementing enterprise data models and quality standards. Data Product Development & AI Readiness · Contribute to the delivery of AI-ready data products by ensuring data is well-structured, versioned, traceable, and semantically aligned with enterprise standards. Incorporate domain definitions, metadata, and relationships that make data interpretable and reusable across scientific and operational use cases. · Implement and maintain semantic mappings and components of semantic layers, working with domain experts and data architects to apply established vocabularies, ontologies, and data models. · Develop and maintain workflows for preparing unstructured content for AI-enabled retrieval, including content extraction, chunking, embedding generation, and vector indexing, while preserving source references and relevant metadata. · Support the population and maintenance of knowledge graphs by transforming source data into entities and relationships and implementing data ingestion and quality checks within established graph models. · Partner with data scientists and ML engineers to understand downstream data requirements and ensure fitness for analytical and AI/ML use cases. · Support metadata management, data cataloging, and lineage documentation. Quality, Compliance & Engineering Practices · Implement data quality checks, validation routines, and monitoring for accuracy, completeness, and consistency. · Follow software development best practices: version control, code review, testing, documentation, and CI/CD integration. · Support data governance and compliance requirements, including traceability and audit readiness.