below marketmost similar roles pay hereabove market
This role pays less than
71%
of similar roles. Most pay
$153,462–$214,365
— the blue band above.
At the midpoint, this role pays about
$159k
versus about
$184k
for comparable roles.
Based on 240 similar postings.
Employer
About Johnson & Johnson
Johnson & Johnson is a multinational corporation operating in three main segments: consumer health products, pharmaceuticals, and medical devices, known for brands like Tylenol, Band-Aid, and Janssen. Industry: Pharmaceuticals & Medical Devices
Johnson & Johnson currently has
46 open roles
on FindRole.
Listed pay typically runs
$113,000–$201,250
across 38 roles with salary data.
The Principal Scientist, Data Science (Data Products, Integration & Analysis) joins the Innovative Medicine team to lead the design and evolution of scientific data products supporting AI-enabled drug discovery and development. This role involves creating scalable, interoperable, and AI-ready data products that connect discovery, preclinical, clinical, safety, and real-world evidence domains. The individual will establish data architecture, integration strategies, and metadata frameworks to support semantic reasoning, knowledge graphs, GraphRAG, and agentic AI applications. Key responsibilities include building predictive AIML models for translational safety decision making and defining data harmonization strategies for SEND, SDTM, ADaM, MedDRA, and Omics data. The position requires expertise in AWS-based data platforms, data modeling, and data governance to deliver high-quality data assets for translational science and patient safety initiatives.
Lead the design and implementation of scientific data products for AI-enabled drug discovery and development.
Create scalable, interoperable data products connecting discovery, preclinical, clinical, safety, and real-world evidence domains.
Establish data architecture, integration strategies, and metadata frameworks to support knowledge graphs and GraphRAG.
Define and execute data product roadmaps aligned with organizational priorities and scientific objectives.
Design integration frameworks and harmonization strategies for heterogeneous sources like Omics, Imaging, and Biomarker data.
Build predictive AI/ML models to support data-driven translational safety decision-making.
Define metadata standards and data quality frameworks including lineage, provenance, and FAIR data principles.
Partner with scientific stakeholders to translate research questions into scalable technical solutions and data assets.
What we're looking for
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.
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, metadata management, data governance, and predictive model development.
Experience with AWS-based data platforms, data lakes/lakehouses, distributed data processing, and data cataloging/lineage solutions.
Strong familiarity with SEND, SDTM, ADaM, and MedDRA standards.
Python
SQL
Spark
Databricks
MLOps
Generative AI
LLMs
CI/CD
GitHub Actions
MLflow
Arize AI
Unity Catalog
Predictive Analytics
Data Pipelines
Feature Engineering
Model Monitoring
Data Governance
Version Control
Anomaly Detection
Machine Learning
Deep Learning
Recommender Systems
Python
TensorFlow
PyTorch
Keras
SQL
AWS
Snowflake
Spark
Google Cloud
NLP
Reinforcement Learning
Causal Inference
Information Retrieval
Collaborative Filtering