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# Principal Scientist, Data Science - R&D DDSAI - Therapeutics Development & Supply (TDS)

- **Company:** Johnson & Johnson
- **Location:** Spring House, PA, United States
- **Experience:** Experienced
- **Salary:** $117,000.0 - $201,250.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Stores, Data Systems, Database Design, DevOps, Dicom, Digital Data, Digital Technology, R (Programming Language), Graph Database, Python (Programming Language), Laboratory Information Management Systems, Machine Learning, NoSQL, Software Tools, Cloud Services, SQL Databases, Data Streaming, Systems Integration, Unstructured Data, Management of Software Versions, Software Organization, Cloud Platform System, Fast Healthcare Interoperability Resources, Snowflake, Information Technology, Data Lineage, Health Level Seven International, Machine Learning Operations, Software Version Control, Data Pipelines, Amazon Redshift, Data Generation
- **Published:** August 14, 2026
- **Apply:** https://www.nexxt.com/job.asp?id=3353294020&tx=UT545THZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Advanced degree in Engineering, Data Science, Life Sciences, Computer Science, or related field; advanced degree preferred.
 * 3+ years of experience in data engineering, including data modeling and database design, preferably in a scientific, manufacturing, or healthcare environment.
 * Proficiency with Python, R, SQL, and cloud-based architectures (e.g., AWS services, Snowflake, Redshift).
 * Experience with NoSQL and graph databases.
 * Strong analytical, problem-solving, and stakeholder-management skills, with the ability to translate discussions into actionable requirements.
 * Ability to drive multiple exciting projects simultaneous with strong organizational skills and adaptability.

Preferred

 * Experience with regulated or standards-driven data environments, such as CDISC, HL7, FHIR, OMOP, DICOM, or manufacturing/quality data standards.
 * Familiarity with high-dimensional data (e.g., imaging, sensor data, etc).
 * Experience with principles connecting to or feeding MLOps and model deployment workflows.
 * Knowledge of manufacturing systems (MES), laboratory information systems, or industrial data systems.
 * Experience with knowledge graph architectures.

## Description

The R&D Data, Data Science and AI organization is seeking a Data Scientist - Data Engineer to design, build, and optimize data capture, processing, and storage solutions that enable advanced analytics, digital process transformation, and AI/ML applications 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, and Digital/Data Science teams to deliver high-quality, AI-ready data pipelines and data products. This role involves creating robust, future-proof data systems, engineering workflows, and high-value data repositories that support scientific, technical, and operational decision-making., Data Engineering & Pipeline Development

 * Design, build, and maintain scalable data pipelines for acquiring, integrating, and managing TDS data from diverse data generation sources and systems (e.g., lab systems, MES, clinical supply, quality systems, external partners).
 * Create and optimize data flows for structured and unstructured data using Python, R, SQL, DBT, cloud services, and other modern engineering tools.
 * Develop and maintain TDS-specific data repositories, implementing enterprise-level data models and creating new models as needed.
 * Enable AI/ML readiness by ensuring data is well-structured, versioned, traceable, and semantically aligned with enterprise data standards.

Data Product & Architecture Partnership

 * Partner with data scientists, TDS domain experts, and digital technology teams to translate business needs into high-quality data products and engineering requirements.
 * Work closely with ontology/knowledge graph teams to implement semantic models and future-proof data architectures.

Quality, Compliance & Performance

 * Implement data quality and performance standards; define KPIs to measure accuracy, completeness, and consistency across TDS data assets.
 * Apply data versioning and lineage tracking for compliance, traceability, and audit readiness.
 * Follow software development best practices including code versioning, DevOps integration, and documentation.

Cross-Functional Collaboration

 * Engage with scientific, technical, and operations stakeholders to understand requirements, design data solutions, and drive adoption.
 * Support multiple concurrent projects, managing priorities and delivering maximum business value across the TDS network.


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