below marketmost similar roles pay hereabove market
This role pays less than
71%
of similar roles. Most pay
$151,031–$214,500
— the blue band above.
At the midpoint, this role pays about
$159k
versus about
$183k
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
53 open roles
on FindRole.
Listed pay typically runs
$117,000–$201,250
across 41 roles with salary data.
The Principal Data Scientist – DDSAI GD joins the Innovative Medicine Data, Data Science, and AI - Global Development team to lead the end-to-end delivery of integrated analytical solutions for clinical-development and trial-operations business problems. This role involves owning solution definition, development, validation, and deployment of models to address study feasibility, enrollment forecasting, and protocol design. The successful candidate will build predictive models, simulations, and GenAI applications using Python, SQL, and modern ML libraries. Key responsibilities include applying real-world data from EHRs, claims, and registries to generate evidence on patient populations and treatment pathways. The position requires expertise in MLOps, NLP, and optimization to automate workflows and interpret clinical concepts while ensuring data privacy and responsible-AI standards across the pharmaceutical development lifecycle.
What does a Data Scientist earn?
Median $173200 from 345 postings across 65 companies.
Own the end-to-end definition, development, validation, deployment, and adoption of integrated data-science solutions.
Lead predictive modeling, simulations, optimization, and GenAI applications for study feasibility and operational planning.
Apply clinical, operational, and real-world data to generate evidence on patient populations and treatment pathways.
Lead RWD analytics to inform protocol feasibility, eligibility criteria, and recruitment and retention strategies.
Develop LLM-enabled workflows for information extraction, data harmonization, and targeted workflow automation.
Partner with stakeholders to translate analytical outputs into adopted recommendations and measurable use-case outcomes.
Apply model-validation, MLOps, privacy, and responsible-AI standards while contributing reusable code and data assets.
Mentor junior colleagues and strengthen delivery practices across project teams.
What we're looking for
Master’s degree in computer science, engineering, biostatistics, health economics, biomedical informatics, applied mathematics, or another quantitative discipline.
At least 5 years of industry experience leading end-to-end delivery of data-science solutions using ML, predictive modeling, optimization, NLP, GenAI, or RWD analytics.
Demonstrated experience defining, validating, deploying, and driving adoption of analytical solutions for clinical-development, clinical-operations, or real-world evidence decisions.
Hands-on experience with structured and unstructured clinical, operational, or RWD data, including multimodal modeling and time-series or simulation methods.
Proficiency in Python and SQL, modern ML, optimization, and LLM-development libraries, and reproducible MLOps practices.
Strong communication, stakeholder-management, and technical-leadership skills.
Experience in healthcare, pharmaceutical development, MedTech, clinical development, or clinical operations (preferred).
Python
SQL
Machine Learning
Deep Learning
Generative AI
Large Language Model (LLM)
PySpark
Spark
Hadoop
Hive
Kafka
Data Bricks
XGBoost
LGBM
NLP
Computer Vision
Statistical Analysis
Data Visualization
SVM
Random Forest
Bayesian Inference
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