Principal Scientist, Data Science (Data Products, Integration & Analysis)
Johnson & Johnson
August 13, 2026
Remote friendly (Titusville, NJ)
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.
Key Responsibilities
- Define and execute scientific data product strategy across discovery research, translational science, preclinical safety, clinical development, pharmacovigilance, and real-world evidence.
- Design integration/harmonization frameworks (SEND, SDTM, ADaM, MedDRA, imaging, omics, biomarker, pathology, real-world data).
- Lead AWS-deployed implementation strategy; ensure alignment with enterprise architecture, security, governance, and AI readiness.
- Develop curated/semantic-ready datasets, feature stores, metadata products, scientific data services, and AI-ready assets; define onboarding/transformation/validation/publication patterns.
- Define metadata/data quality standards; implement lineage/provenance/traceability and FAIR principles.
- Build predictive AIML models to support translational safety decision-making.
- Partner with scientific and technical stakeholders to translate scientific questions into scalable solutions.
Required Qualifications
- Master’s or PhD in CS, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or related discipline.
- 5+ years in scientific data engineering/architecture/products or life sciences informatics.
- Experience designing enterprise-scale scientific data products; supporting drug discovery/development, clinical research, or pharmacovigilance.
- Experience developing predictive models in those domains.
Preferred Qualifications
- Knowledge graphs/semantic architectures/GraphRAG; AI-ready data products/feature stores; ontology-driven integration.
- Partnering with cloud providers/external platform teams; regulated scientific environments.
Benefits (time off)
- Vacation (120 hrs/yr); Sick time (40 hrs/yr; CO 48; WA 56); Holiday pay incl. floating (13 days/yr); Work/Personal/Family Time (up to 40 hrs/yr); Parental Leave (480 hrs/1 year); Bereavement Leave (240 hrs immediate family; extended 40 hrs/yr); Caregiver Leave (80 hrs/52-week period); Volunteer Leave (32 hrs/yr); Military Spouse Time-Off (80 hrs/yr).
- 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.
Key Responsibilities
- Define and execute scientific data product strategy across discovery research, translational science, preclinical safety, clinical development, pharmacovigilance, and real-world evidence.
- Design integration/harmonization frameworks (SEND, SDTM, ADaM, MedDRA, imaging, omics, biomarker, pathology, real-world data).
- Lead AWS-deployed implementation strategy; ensure alignment with enterprise architecture, security, governance, and AI readiness.
- Develop curated/semantic-ready datasets, feature stores, metadata products, scientific data services, and AI-ready assets; define onboarding/transformation/validation/publication patterns.
- Define metadata/data quality standards; implement lineage/provenance/traceability and FAIR principles.
- Build predictive AIML models to support translational safety decision-making.
- Partner with scientific and technical stakeholders to translate scientific questions into scalable solutions.
Required Qualifications
- Master’s or PhD in CS, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or related discipline.
- 5+ years in scientific data engineering/architecture/products or life sciences informatics.
- Experience designing enterprise-scale scientific data products; supporting drug discovery/development, clinical research, or pharmacovigilance.
- Experience developing predictive models in those domains.
Preferred Qualifications
- Knowledge graphs/semantic architectures/GraphRAG; AI-ready data products/feature stores; ontology-driven integration.
- Partnering with cloud providers/external platform teams; regulated scientific environments.
Benefits (time off)
- Vacation (120 hrs/yr); Sick time (40 hrs/yr; CO 48; WA 56); Holiday pay incl. floating (13 days/yr); Work/Personal/Family Time (up to 40 hrs/yr); Parental Leave (480 hrs/1 year); Bereavement Leave (240 hrs immediate family; extended 40 hrs/yr); Caregiver Leave (80 hrs/52-week period); Volunteer Leave (32 hrs/yr); Military Spouse Time-Off (80 hrs/yr).