Principal Scientist, Data Science (Data Products, Integration & Analysis)
Johnson & Johnson
August 13, 2026
Remote friendly (Horsham, PA)
United States
IT
Position Summary:
- Lead design, implementation, and evolution of scientific data products and integration strategies for AI-enabled drug discovery and development.
- Create scalable, interoperable, AI-ready data products connecting discovery, preclinical, clinical, safety, and real-world evidence.
- Establish data architecture, integration strategy, metadata framework, and productization approach for semantic reasoning, knowledge graphs, GraphRAG, advanced analytics, and agentic AI.
- Work with scientific stakeholders, knowledge architects, AI engineers, and platform teams to deliver high-quality products supporting translational science and patient safety.
Key Responsibilities:
- Define and execute scientific data product strategy (discovery, translational science, preclinical safety, clinical development, pharmacovigilance, real-world evidence).
- Design data integration/harmonization frameworks (SEND, SDTM, ADaM, MedDRA, Imaging, Omics, Biomarker, Pathology, real-world data).
- Lead AWS deployment strategy; partner on pipelines/data products; ensure security/governance/AI-readiness alignment.
- Develop curated datasets, semantic-ready data products, feature stores, metadata products, scientific data services, and AI-ready assets.
- Define metadata standards; implement lineage/provenance/traceability and FAIR; monitoring and quality controls.
- Build predictive AIML models for translational safety decision making.
Required Qualifications:
- Master’s or PhD in CS/Data Engineering/Bioinformatics/Biomedical Informatics/Information Systems/Computational Biology/related.
- 5+ years in scientific data engineering/architecture/products or life sciences informatics.
- Enterprise-scale scientific data product delivery experience.
- Experience supporting drug discovery/development/clinical research/pharmacovigilance.
- Experience developing predictive models in these areas.
Preferred Qualifications:
- Knowledge graphs/semantic architectures/GraphRAG; AI-ready products/feature stores; ontology-driven integration; cloud/platform partnerships; highly regulated scientific environments.
Skills (Required/Preferred listed): Advanced analytics, critical thinking, data quality/analysis/reporting/visualization, data governance & metadata, data architecture/modeling/product design, cloud-native platforms, predictive model development.
Benefits (time off): Vacation 120h/yr; Sick time 40h/yr (48h CO; 56h WA); Holiday pay 13 days/yr; Parental leave 480h (within one year); other stated leave options included.
- Lead design, implementation, and evolution of scientific data products and integration strategies for AI-enabled drug discovery and development.
- Create scalable, interoperable, AI-ready data products connecting discovery, preclinical, clinical, safety, and real-world evidence.
- Establish data architecture, integration strategy, metadata framework, and productization approach for semantic reasoning, knowledge graphs, GraphRAG, advanced analytics, and agentic AI.
- Work with scientific stakeholders, knowledge architects, AI engineers, and platform teams to deliver high-quality products supporting translational science and patient safety.
Key Responsibilities:
- Define and execute scientific data product strategy (discovery, translational science, preclinical safety, clinical development, pharmacovigilance, real-world evidence).
- Design data integration/harmonization frameworks (SEND, SDTM, ADaM, MedDRA, Imaging, Omics, Biomarker, Pathology, real-world data).
- Lead AWS deployment strategy; partner on pipelines/data products; ensure security/governance/AI-readiness alignment.
- Develop curated datasets, semantic-ready data products, feature stores, metadata products, scientific data services, and AI-ready assets.
- Define metadata standards; implement lineage/provenance/traceability and FAIR; monitoring and quality controls.
- Build predictive AIML models for translational safety decision making.
Required Qualifications:
- Master’s or PhD in CS/Data Engineering/Bioinformatics/Biomedical Informatics/Information Systems/Computational Biology/related.
- 5+ years in scientific data engineering/architecture/products or life sciences informatics.
- Enterprise-scale scientific data product delivery experience.
- Experience supporting drug discovery/development/clinical research/pharmacovigilance.
- Experience developing predictive models in these areas.
Preferred Qualifications:
- Knowledge graphs/semantic architectures/GraphRAG; AI-ready products/feature stores; ontology-driven integration; cloud/platform partnerships; highly regulated scientific environments.
Skills (Required/Preferred listed): Advanced analytics, critical thinking, data quality/analysis/reporting/visualization, data governance & metadata, data architecture/modeling/product design, cloud-native platforms, predictive model development.
Benefits (time off): Vacation 120h/yr; Sick time 40h/yr (48h CO; 56h WA); Holiday pay 13 days/yr; Parental leave 480h (within one year); other stated leave options included.