Sr. Scientist, Global Commercial Data Science (GCDS)

Johnson & Johnson-posted about 1 year ago
$113,000 - $195,500/Yr
Full-time • Senior
Raritan, NJ
Chemical Manufacturing
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The Senior Scientist in Global Commercial Data Science at Johnson & Johnson will design, develop, and deliver impactful data science projects that provide insights and analytics to shape the commercial development of the clinical-stage pipeline. This role requires deep technical expertise and pharmaceutical industry knowledge to apply leading data science methodologies to domain-specific data assets, ultimately driving insights and decisions that optimize patient benefits and commercial value.

  • Act as a hands-on, technical contributor to conceive, design, build, and implement state-of-the-art scientific algorithms.
  • Lead and deliver data science projects by developing and applying creative solutions that go beyond current tools to deliver data-driven insights for high-priority global commercial analytic business questions.
  • Define data and analytics strategies and methods for specific Data Science projects across GCSO.
  • Use AI/ML to transform our commercial strategy operations through impactful analytics projects that improve GCSO strategic decisions.
  • Effectively navigate technological, analytical, legal, and privacy complexities to achieve objectives.
  • Participate in project teams and collaborate closely with asset and disease area teams across Commercial, Medical Affairs, and Market Access on outcomes and insights from specific data science projects.
  • Partner with other GCSO functions, regional businesses, global epidemiology, R&D, JJT, Legal, HCC and others in the development of the Data Science strategy and guidelines for insight generation and usage.
  • Contribute actively to internal J&J Data Science communities of practice, sharing methodologies and improving enterprise Data Science knowledge and capacity.
  • Provide technical guidance to other team members.
  • Work side-by-side with colleagues across GCDS to deliver integrated approaches to deliver on high value use cases/problems.
  • Master's degree in a quantitative field such as computer science, computational biology, statistics, epidemiology or a related discipline; PhD/PharmD/MD preferred.
  • At least 3 years of relevant experience in healthcare or life sciences industry is required.
  • At least 3 years of relevant hands-on data science experience in a commercial, medical affairs, scientific affairs and/or R&D environment is required.
  • Knowledge of machine learning, deep learning, and statistical modeling methods for supervised and unsupervised learning, including regression analysis, clustering, outlier detection, decision trees, ensemble methods and boosting, neural networks, generative AI is required.
  • Familiarity with large datasets, understanding of data analysis workflows, and/or knowledge of querying languages such as SQL is required.
  • Handling of healthcare relevant datasets, such as EHR, insurance claims or registry data, is required.
  • Proficient with one or more programming languages such as Python, R, C++, or Java is required.
  • Experience delivering on projects using large scale LLMs is preferred.
  • Experience with visualization software/tools such as R, Spotfire, Tableau, etc. is preferred.
  • Specific data science experience within Oncology or Immunology preferred.
  • Strong problem-solving skills with a solution-oriented mindset.
  • Proven experience in prioritizing and leading a complicated portfolio of projects.
  • Excellent communication, interpersonal, and written skills are required.
  • Passion for innovation, drive to create the best solutions and generate a positive impact for patients.
  • Experience delivering on projects using large scale LLMs is preferred.
  • Experience with visualization software/tools such as R, Spotfire, Tableau, etc. is preferred.
  • Specific data science experience within Oncology or Immunology preferred.
  • Competitive performance-based compensation programs.
  • Annual performance bonus based on individual and corporate performance.
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