Principal Scientist, Data Science (Data Products, Integration & Analysis)

Johnson & Johnson Co.; Spring House (PA); On-site; USD 150,000 - 230,000; Full time

Job summary

Johnson & Johnson seeks a Principal Scientific Data Scientist to design and evolve AI-ready scientific data products that support drug discovery and development. You will build scalable, interoperable data assets that connect discovery, preclinical, clinical, safety and real-world data, and establish data architecture, metadata, and productization approaches.

Work with scientists, knowledge architects, AI engineers, and AWS/BioDiscovery teams to shape the future data ecosystem, ensuring

Responsibilities

• Lead design and evolution of scientific data products for AI-enabled drug discovery and development.
• Create scalable, interoperable data assets connecting discovery, preclinical, clinical, safety and real-world evidence.
• Define data architecture, integration strategy, metadata framework, and productization approach.
• Establish patterns for data onboarding, transformation, validation, and publication.
• Provide technical leadership and ensure solutions meet governance, security, and AI-readiness requirements.
• Collaborate with scientists, data scientists, AI engineers and platform teams.

Skills

AI/ML

Data analytics

Tools

AWS

Job description

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.

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

Horsham, Pennsylvania, United States of America, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

About Innovative Medicine

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine

Position Summary

The Principal Scientific Data Scientist will lead the design, implementation, and evolution of scientific data products and integration strategies supporting AI-enabled drug discovery and development.

This individual will be responsible for creating scalable, interoperable, and AI-ready data products that connect discovery, preclinical, clinical, safety, and real-world evidence domains, and enable the creation of validated-biomarker data assets . The role will establish the data architecture, integration strategy, metadata framework, and productization approach needed to support semantic reasoning, knowledge graphs, GraphRAG, advanced analytics, and agentic AI applications.

Working closely with scientific stakeholders, knowledge architects, AI engineers, and Amazon BioDiscovery platform teams, this individual will define the future-state scientific data ecosystem and ensure high-quality data products are delivered to support translational science and patient safety initiatives.Build AI reasoning models to support data-driven translational safety decision making.

Mission

Build and operationalize AI-ready scientific data products that enable seamless integration, harmonization, and reuse of data across the drug discovery and development lifecycle.

Key Responsibilities

• Scientific Data Product Strategy
  • Define and execute a scientific data product strategy supporting:
    • Discovery Research
    • Translational Science
    • Preclinical Safety
    • Clinical Development
    • Pharmacovigilance
    • Real-World Evidence
  • Establish reusable, scalable data products that support analytics, AI, knowledge graph, and scientific reasoning use cases.
  • Develop product roadmaps aligned with organizational priorities and scientific objectives.
• Data Integration Architecture
  • Design integration frameworks connecting heterogeneous scientific data sources.
  • Define data harmonization strategies spanning:
    • SEND
    • SDTM
    • ADaM
    • MedDRA
    • Imaging
    • Omics
    • Biomarker
    • Pathology
    • Real-world data
  • Create architecture patterns supporting cross-domain data interoperability.
• Digital Platform Leadership
  • Define the implementation strategy for scientific data products deployed on AWS
  • Partner with Amazon engineering and deployed platform resources to deliver scalable data pipelines and data products.
  • Provide technical leadership and architectural oversight for implementation activities aligned recommendations from the Data Strategy group.
  • Ensure digital solutions align with enterprise architecture, security, governance, and AI-readiness requirements.
• Data Product Development
  • In collaboration with Data Strategy group, lead design and implementation of:
    • Curated datasets
    • Semantic-ready data products
    • Feature stores
    • Metadata products
    • Scientific data services
    • AI-ready data assets
  • Establish reusable patterns for data onboarding, transformation, validation, and publication.
• Data Quality & Metadata
  • Define metadata standards and data quality frameworks.
  • Implement lineage, provenance, traceability, and FAIR data principles.
  • Establish monitoring and quality controls for scientific data products.
• Data Analysis:
  • Build predictive AIML models to support translational safety decision making.
• Stakeholder Engagement
  • Partner with:
    • Discovery Scientists
    • Toxicologists
    • Clinical Scientists
    • Safety Scientists
    • Data Scientists and Data Strategy business partners.