Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 5 and day 54.
From 10,736 similar postings that came down. Coming down is not always a hire.
Madrid, Spain
Actively hiring, first seen by us todayThe anticipated base pay range for this position is: €56,000.00 - €88,780.00 Benefits:Read the full posting
Employment type in the employer’s posting: Full timeFrom the employer’s posting
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.
Build and maintain data pipelines that clean and transform TDS data from ELN, MES, LIMS and SAP
Develop reusable, governed data products with metadata and semantic models
Prepare unstructured content for AI retrieval via chunking, embeddings and vector indexing
Support knowledge graph population and data quality checks
Implement data quality checks, validation routines and monitoring
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.
Build and maintain data pipelines for integrating, cleaning, and transforming TDS data from diverse sources and systems (ELNs, MES, LIMS, SAP
The role includes developing data products that preserve scientific and operational context through metadata, semantic models, and relationships between entities
Develop and maintain workflows for preparing unstructured content for AI-enabled retrieval, including content extraction, chunking, embedding generation, and vector indexing
Support the population and maintenance of knowledge graphs by transforming source data into entities and relationships
Implement data quality checks, validation routines, and monitoring for accuracy, completeness, and consistency.
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.
· 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.
From the employer’s posting.
Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 0
Early. Similar jobs usually come down between day 5 and day 54.
From 10,736 similar postings that came down. Coming down is not always a hire.
74 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
· 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.
· 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.
· 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.
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.
Complete your application on jj.wd5.myworkdayjobs.com. The employer’s form will show what is required.
Already applied? Track this application
Yes. We’ve followed 2 other Senior Scientist, Data Science jobs there since July 2026. One came down after 0 days. One is still up.
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com.
As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.
Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s):
Belgium R-108690, Spain R-104989
Remember, whether you apply to one or all of these requisition numbers, your applications will be considered as a single submission.
Job Function:
Data Analytics & Computational SciencesJob Sub Function:
Data ScienceJob Category:
Scientific/TechnologyAll Job Posting Locations:
Beerse, Antwerp, Belgium, Madrid, Spain, Turnhout, Antwerp, BelgiumJob Description:
Job Description:
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/
POSITION SUMMARY
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.
KEY RESPONSIBILITIES
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.
QUALIFICATIONS
Required
· 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.
Why This Role Is Unique
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.
#LI-GR
#LI-Hybrid
#JRDDS
#JNJDataScience
#JRD
Required Skills:
Preferred Skills:
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
The anticipated base pay range for this position is:
€56,000.00 - €88,780.00
Benefits:
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.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
The anticipated base pay range for this position is: €56,000.00 - €88,780.00 Benefits:
Madrid, Spain
As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s): Belgium R-108690, Spain R-104989
#LI-GR #LI-Hybrid #JRDDS
No clear work-authorization passage found. Eligibility is unconfirmed.