Principal Scientist, Data Science

Biopharma Asia; Pennsylvania; Sur place; USD 150 000 - 220 000; Plein temps

Résumé du poste

Johnson & Johnson’s Innovative Medicine data science organization seeks a Principal Scientific Data Scientist to lead AI-ready data products across discovery to pharmacovigilance. You will shape data architecture, integration, and metadata frameworks to connect discovery, preclinical, clinical, safety, and real-world evidence domains.

Work with scientists, AI engineers, and platform teams to deliver scalable data products on AWS, enabling semantic reasoning, knowledge graphs, and advanced

Qualifications

• 5+ years of experience in scientific data engineering, data architecture, data products, or life sciences informatics.
• Demonstrated experience designing and delivering enterprise-scale scientific data products.
• Experience supporting drug discovery, development, clinical research, or pharmacovigilance organizations.
• Experience developing predictive models in drug discovery, development, clinical research, or pharmacovigilance organizations.
• Strong expertise in data architecture, data modeling, data product design, cloud-native data platforms, metadata management, data governance, and predictive model development.

Responsabilités

• Define and execute a scientific data product strategy supporting Discovery Research, Translational Science, Preclinical Safety, Clinical Development, Pharmacovigilance and Real-World Evidence.
• Establish reusable, scalable data products that support analytics, AI, knowledge graphs, and scientific reasoning use cases.
• Design integration frameworks connecting heterogeneous scientific data sources.
• Define data harmonization spanning SEND, SDTM, ADaM, MedDRA, Imaging, Omics, Biomarker, Pathology, Real-world data.
• Define implementation strategy for scientific data products deployed on AWS.
• Partner with AWS engineering and deployed platform resources to deliver scalable data pipelines.
• Provide technical leadership and architectural oversight for data strategy recommendations.
• Ensure digital solutions align with enterprise architecture, security, governance, and AI-readiness.

Connaissances

Data architecture

Data modeling

Data product design

Cloud-native data platforms

Metadata management

Data governance

Predictive model development

AWS

Formation

Master’s or PhD in Computer Science

Master’s or PhD in Data Engineering

Master’s or PhD in Bioinformatics

Master’s or PhD in Biomedical Informatics

Master’s or PhD in Information Systems

Master’s or PhD in Computational Biology

Master’s or PhD in Related scientific discipline

Outils

AWS

Description du poste

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions 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
• Knowledge Architects
• AI Engineers

Translate scientific questions into scalable data products and technical solutions.

Required Qualifications

Education

Master’s or PhD in:

• Computer Science
• Data Engineering
• Bioinformatics
• Biomedical Informatics
• Information Systems
• Computational Biology
• Related scientific discipline

Experience

5+ years of experience in scientific data engineering, data architecture, data products, or life sciences informatics.

Demonstrated experience designing and delivering enterprise-scale scientific data products.

Experience supporting drug discovery, development, clinical research, or pharmacovigilance organizations.

Experience developing predictive models in drug discovery, development, clinical research, or pharmacovigilance organizations.

Technical Expertise

Strong expertise in:

• Data architecture
• Data modeling
• Data product design
• Cloud-native data platforms
• Metadata management
• Data governance
• Predictive model development

Experience with:

• AWS