Senior Scientist, Data Science in Madrid

€56,000.00 - €88,780.00

In addition to base pay, we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location, where the actual amount is based on the employees' and companies' performance of the previous calendar year, or sales commissions. Moreover, we offer vacation days, parental leave for a minimum of 12 weeks, bereavement leave, caregiver leave, volunteer leave, well-being reimbursement, programs for financial, physical and mental health. We also offer service anniversary and recognition awards, and subject to the terms of their respective plans, employees - and in some location's eligible dependents - can participate in several insurance plans. For more information, visit Employee benefits | Supporting well-being & career growth | Johnson & Johnson Careers.

*This is for informative purposes only. Amounts and actual benefits may vary by location and are subject to change.

Job Function: Data Analytics & Computational Sciences

Job Sub Function: Data Science

Job Category: Scientific/Technology

Johnson & Johnson Innovative Medicine is recruiting for Senior Scientist, Data Science - R&D DDSAI - Therapeutics Development & Supply (TDS).

The primary location for this position is open to: Beerse, Belgium or Madrid, Spain

J&J Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, immunology, neuroscience, cardiopulmonary and specialty ophthalmology. Our goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market, from patients to practitioners and from clinics to hospitals. To learn more about Johnson & Johnson Innovative Medicine visit https://innovativemedicine.jnj.com/

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.

This role offers the opportunity to build reusable data capabilities that connect scientific and operational data with the context needed for analytics and AI. Working across Therapeutics Development & Supply, you will contribute directly to practical applications that support pharmaceutical development, manufacturing, and supply decisions.

• 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.
• 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.
• 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.