Principal Scientist, Data Science – R&D DDSAI - Therapeutics Development & Supply (TDS)
Johnson & Johnson
August 11, 2026
On-site
Spring House, PA
IT
Position Summary:
Data Scientist – Data Engineer (R&D DDSAI – Therapeutics Development & Supply, TDS). Design, build, and optimize data capture, processing, and storage solutions enabling advanced analytics, digital transformation, and AI/ML across the development-to-supply continuum.
Key Responsibilities:
- Design, build, and maintain scalable data pipelines integrating TDS data from lab systems, MES, clinical supply, quality systems, and external partners.
- Create/optimize structured and unstructured data flows using Python, R, SQL, DBT, cloud services, and modern engineering tools.
- Build and maintain TDS data repositories; implement enterprise data models.
- Ensure AI/ML readiness (well-structured, versioned, traceable, semantically aligned data).
- Partner with data scientists and domain experts to define data products and engineering requirements.
- Implement semantic/knowledge graph models and future-proof architectures.
- Establish data quality/performance standards; define KPIs (accuracy, completeness, consistency).
- Apply data versioning and lineage for compliance, traceability, and audit readiness; follow DevOps, documentation, and code versioning best practices.
Qualifications:
Required:
- Advanced degree (Engineering/Data Science/Life Sciences/CS or related); advanced degree preferred.
- 3+ years data engineering (data modeling/database design), preferably in scientific/manufacturing/healthcare.
- Proficiency: Python, R, SQL; cloud architectures (e.g., AWS, Snowflake, Redshift).
- Experience with NoSQL and graph databases.
- Strong analytical/problem-solving and stakeholder management; translate needs into requirements.
Preferred:
- Regulated/standards-driven data (CDISC, HL7, FHIR, OMOP, DICOM, manufacturing/quality).
- High-dimensional data (imaging/sensor).
- Knowledge of MLOps/model deployment workflows.
- Manufacturing systems (MES), lab information systems, or industrial data systems.
- Knowledge graph architecture experience.
Required Skills:
Advanced Analytics, Data Analysis, Data Quality, Data Reporting, Data Science, Data Visualization, Digital Fluency, Critical Thinking, Technical Credibility, Workflow Analysis, Data Privacy Standards.
Preferred Skills:
Coaching, Data Savvy, Econometric Models, Organizing, Process Improvements, Strategic Thinking.
Benefits (time off):
Vacation (120 hrs/yr), Sick time (40 hrs/yr; CO/Washington varies), Holiday pay incl. Floating Holidays (13 days/yr), Work/Personal/Family Time (up to 40 hrs/yr), Parental Leave (480 hrs/yr), Bereavement Leave (240 hrs/yr immediate family; 40 hrs extended family), Caregiver Leave (80 hrs in 52-week period), Volunteer Leave (32 hrs/yr), Military Spouse Time-Off (80 hrs/yr).
Data Scientist – Data Engineer (R&D DDSAI – Therapeutics Development & Supply, TDS). Design, build, and optimize data capture, processing, and storage solutions enabling advanced analytics, digital transformation, and AI/ML across the development-to-supply continuum.
Key Responsibilities:
- Design, build, and maintain scalable data pipelines integrating TDS data from lab systems, MES, clinical supply, quality systems, and external partners.
- Create/optimize structured and unstructured data flows using Python, R, SQL, DBT, cloud services, and modern engineering tools.
- Build and maintain TDS data repositories; implement enterprise data models.
- Ensure AI/ML readiness (well-structured, versioned, traceable, semantically aligned data).
- Partner with data scientists and domain experts to define data products and engineering requirements.
- Implement semantic/knowledge graph models and future-proof architectures.
- Establish data quality/performance standards; define KPIs (accuracy, completeness, consistency).
- Apply data versioning and lineage for compliance, traceability, and audit readiness; follow DevOps, documentation, and code versioning best practices.
Qualifications:
Required:
- Advanced degree (Engineering/Data Science/Life Sciences/CS or related); advanced degree preferred.
- 3+ years data engineering (data modeling/database design), preferably in scientific/manufacturing/healthcare.
- Proficiency: Python, R, SQL; cloud architectures (e.g., AWS, Snowflake, Redshift).
- Experience with NoSQL and graph databases.
- Strong analytical/problem-solving and stakeholder management; translate needs into requirements.
Preferred:
- Regulated/standards-driven data (CDISC, HL7, FHIR, OMOP, DICOM, manufacturing/quality).
- High-dimensional data (imaging/sensor).
- Knowledge of MLOps/model deployment workflows.
- Manufacturing systems (MES), lab information systems, or industrial data systems.
- Knowledge graph architecture experience.
Required Skills:
Advanced Analytics, Data Analysis, Data Quality, Data Reporting, Data Science, Data Visualization, Digital Fluency, Critical Thinking, Technical Credibility, Workflow Analysis, Data Privacy Standards.
Preferred Skills:
Coaching, Data Savvy, Econometric Models, Organizing, Process Improvements, Strategic Thinking.
Benefits (time off):
Vacation (120 hrs/yr), Sick time (40 hrs/yr; CO/Washington varies), Holiday pay incl. Floating Holidays (13 days/yr), Work/Personal/Family Time (up to 40 hrs/yr), Parental Leave (480 hrs/yr), Bereavement Leave (240 hrs/yr immediate family; 40 hrs extended family), Caregiver Leave (80 hrs in 52-week period), Volunteer Leave (32 hrs/yr), Military Spouse Time-Off (80 hrs/yr).