Director, Data, Data Science and Artificial Intelligence – Oncology / Bladder Cancer

Johnson & Johnson Johnson & Johnson · Pharma · Spring House, PA +7

Director of Data, Data Science, and AI for Oncology/Bladder Cancer at Johnson & Johnson. This role focuses on developing and deploying AI/ML solutions to support late-stage clinical development, including novel AI endpoints, analytics for study design, and digital pathology approaches. The position involves leading multidisciplinary teams, managing external partnerships, and driving innovation in Oncology therapeutic areas to enhance patient outcomes and support regulatory submissions.

What you'd actually do

  1. Serve as primary scientific and strategic business leader for clinical-stage Bladder Cancer Program within Data, Data Science, and AI
  2. Represent Data Science to Compound Team(s), where you will identify, develop and deploy Data Science solutions, including program and trial AI strategy to support clinical development.
  3. Provide subject matter expertise in bladder cancer clinical development, trial design, real-world evidence generation, and ML/AI tools.
  4. With internal and external teams, leverage advanced analytics, including statistical models and AI/ML, to uncover insights from these complex datasets
  5. Work closely with individual clinical project teams as well as functional area partners in Regulatory Affairs, Medical Affairs, Clinical Development, Epidemiology, Commercial Data Science, Statistics, and others to deploy Data Science solutions.

Skills

Required

  • PhD, MD, PharmD or MS in Data Science, Pharmacoepidemiology, Biostatistics, Outcomes Research, Public Health, Computer Science or related field
  • 8+ years of experience in drug development or related discipline in biotechnology, AI, omics, or real-world evidence, or 8 years in clinical practice with outcomes research experience
  • Working knowledge of clinical oncology
  • Understanding of common data science research practices (predictive technologies, data mining, text mining)
  • Hands-on experience with data science use cases in R&D (e.g., ECAs, site selection algorithms)
  • Experience working with healthcare-related datasets (EHR, omics, imaging)
  • Prior experience working and driving external partnerships
  • Proven track record of managing timelines and driving key deliverables

What the JD emphasized

  • AI/ML tools
  • AI strategy
  • AI endpoints
  • advanced analytics
  • AI/ML

Other signals

  • AI/ML for clinical trials
  • AI endpoints
  • digital health technologies
  • advanced statistical methodologies
  • patient-centricity
  • regulatory submissions