This role pays less than
65%
of similar roles. Most pay
$204,293–$278,000
— the shaded band above.
At the midpoint, this role pays about
$223k
versus about
$241k
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
$117,000–$201,250
across 42 roles with salary data.
Director, Data, Data Science and Artificial Intelligence – Oncology / Bladder Cancer joins the R&D Data, Data Science, and AI team to lead initiatives for the Solid Tumor Oncology portfolio. This role focuses on the bladder cancer program, where the individual will direct efforts to integrate advanced data science into late-stage compound development. Key responsibilities include developing AI strategies for clinical trials, identifying novel AI endpoints, utilizing digital pathology for translational research, and managing external partnerships. The candidate will work with cross-functional teams to leverage machine learning, statistical models, and high-dimensional datasets such as electronic health records, omics, and imaging data. Essential skills include expertise in predictive technologies, data mining, and clinical oncology. This role solves complex problems in trial design and real-world evidence generation to improve patient outcomes within the bladder cancer therapeutic area.
Serve as the primary scientific and strategic leader for clinical-stage Bladder Cancer programs within the Data, Data Science, and AI department.
Identify, develop, and deploy data science solutions and AI strategies to support clinical development and trial execution.
Provide subject matter expertise in bladder cancer clinical development, trial design, real-world evidence generation, and ML/AI tools.
Utilize advanced analytics, statistical models, and machine learning to extract insights from complex healthcare datasets.
Collaborate with cross-functional teams in Regulatory, Medical, Clinical Development, and Statistics to implement data science solutions.
Evaluate and manage external partnerships to leverage new technologies, data assets, and innovative solutions for program needs.
Act as a thought leader to drive strategic collaborations and elevate data science capabilities across the Oncology therapeutic area.
What we're looking for
Hold an advanced degree (PhD, MD, PharmD, or MS) in a field related to high-dimensional datasets such as Data Science, Biostatistics, or Computer Science.
Possess at least 8 years of experience in drug development, biotechnology, artificial intelligence, omics, real-world evidence, or clinical practice with outcomes research.
Demonstrate working knowledge of clinical oncology, including treatment paradigms and patient experiences.
Understand common data science research practices such as predictive technologies, data mining, and text mining.
Have hands-on experience with R&D data science use cases like efficacy studies and site selection algorithms.
Experience working with healthcare datasets including electronic health records (EHR), omics, or imaging data is required.
Possess a proven track record of managing timelines and deliverables within a matrixed organization.
Exhibit strong communication, interpersonal, and written skills to lead multidisciplinary teams and manage external partnerships.
Data Science
Artificial Intelligence
Generative AI
Agentic Workflows
Python
R
SQL
Cloud Platforms
Machine Learning
Digital Health
Workflow Automation
Real-World Data
Imaging
Omics
Analytics
Data Platforms
AI
Machine Learning
Data Science
R
Biostatistics
Bayesian Methods
Causal Inference
Longitudinal Analysis
Digital Health
Neuroimaging
Omics
Real-World Data
Data Privacy Standards
Predictive Modeling
Multimodal Integration
Machine Learning
Large Language Models
Generative AI
Natural Language Processing
Python
SQL
Deep Learning
Model Evaluation
Clinical Data
EMR Systems
Data Science
Responsible AI
HIPAA