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Principal Scientist, Data Science – Data Products, Integration & Analysis
Johnson & JohnsonCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing AI-enabled scientific data products and integration strategies, with a strong focus on data architecture, predictive modeling, and compliance with industry standards. Proven ability to lead cross-functional teams and translate scientific requirements into scalable technical solutions.
Highest-signal resume keywords
Data ArchitecturePredictive Model DevelopmentAWS-Based Data PlatformsScientific Data Product DesignKnowledge Graphs
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Lead the design, implementation, and evolution of scientific data products and integration strategies for AI-enabled drug discovery and development
- Create scalable, interoperable, and AI-ready data products connecting discovery, preclinical, clinical, safety, and real-world evidence domains
- Establish data architecture, integration strategy, metadata framework, and productization approach for semantic reasoning, knowledge graphs, GraphRAG, advanced analytics, and agentic AI
- Build AI reasoning models and predictive AI/ML models for data-driven translational safety decision-making
- Define and execute scientific data product strategies supporting discovery research, translational science, preclinical safety, clinical development, pharmacovigilance, and real-world evidence
- Establish reusable data products, product roadmaps, and scalable patterns for analytics, AI, knowledge graphs, and scientific reasoning
- Design integration frameworks and harmonization strategies across SEND, SDTM, ADaM, MedDRA, imaging, omics, biomarker, pathology, and real-world data
- Define implementation strategies for scientific data products deployed on AWS
- Partner with Amazon engineering and platform resources to deliver scalable data pipelines and data products
- Provide technical leadership and architectural oversight aligned with Data Strategy recommendations
- Ensure digital solutions meet enterprise architecture, security, governance, and AI-readiness requirements
- Lead development of curated datasets, semantic-ready data products, feature stores, metadata products, scientific data services, and AI-ready data assets
- Establish patterns for data onboarding, transformation, validation, and publication
- Define metadata standards and data quality frameworks
- Implement lineage, provenance, traceability, FAIR data principles, monitoring, and quality controls
- Partner with discovery scientists, toxicologists, clinical scientists, safety scientists, data scientists, Data Strategy partners, knowledge architects, and AI engineers
- Translate scientific questions into scalable data products and technical solutions
Requirements
What you’ll need- Master’s or PhD in Computer Science, Data Engineering, Bioinformatics, Biomedical Informatics, Information Systems, Computational Biology, or a related scientific discipline
- 5+ years of experience in scientific data engineering, data architecture, data products, or life sciences informatics
- 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
- Experience with AWS-based data platforms, data lakes and lakehouses, distributed data processing, APIs and data services, and data cataloging and lineage solutions
- Strong familiarity with SEND, SDTM, ADaM, and MedDRA
- Preferred familiarity with FHIR, OMOP, DICOM, and biomarker and omics data standards
- Preferred experience with knowledge graphs, semantic architectures, GraphRAG initiatives, AI-ready data products, feature stores, ontology-driven data integration, cloud providers or external platform teams, and regulated scientific environments
- Ability to define long-term data product roadmaps
- Ability to bridge scientific and technical communities and influence cross-functional teams without direct authority
Benefits
Comp & perks- Retirement plan (pension)
- 401(k) savings plan
- Long-term incentive program
- Vacation – 120 hours per calendar year
- Sick time – 40 hours per calendar year; 48 hours for Colorado employees; 56 hours for Washington employees
- Holiday pay, including floating holidays – 13 days per calendar year
- Work, Personal and Family Time – up to 40 hours per calendar year
- Parental Leave – 480 hours within one year of birth/adoption/foster care of a child
- Bereavement Leave – 240 hours for an immediate family member; 40 hours for an extended family member per calendar year
- Caregiver Leave – 80 hours in a 52-week rolling period
- Volunteer Leave – 32 hours per calendar year
- Military Spouse Time-Off – 80 hours per calendar year
- Inclusive interview accommodations for applicants with disabilities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringData ModelingMetadata ManagementData GovernanceCloud-Native Data PlatformsSemantic ReasoningGraphRAGAdvanced AnalyticsFeature StoresData Quality Frameworks
Soft Skills
Cross-Functional Team InfluenceTechnical LeadershipCommunication
Tools & Technologies
AWSData LakesAPIsData CatalogingDistributed Data Processing
Industry Keywords
Drug DiscoveryClinical ResearchPharmacovigilanceSENDSDTMADaMMedDRAFHIROMOPDICOM