Principal Data Scientist, Real World Evidence

Johnson & Johnson Johnson & Johnson · Pharma · Madrid, Spain +1

Principal Data Scientist, Real World Evidence (RWE) at Johnson & Johnson Innovative Medicine. This role focuses on developing evidence solutions and insights from diverse data sources (RWD, trial data) to support clinical programs and regulatory submissions, particularly in Neuroscience. Responsibilities include conceptualizing, designing, analyzing, and interpreting RWE studies, leveraging machine learning/deep learning, and applying graph neural networks. Requires a PhD or Master's in a quantitative field, experience in biopharma/healthcare, and proficiency in R or Python, SPARQL, Cypher.

What you'd actually do

  1. Contribute to the development of a portfolio of RWE projects based on RWD that will provide key insights to our pipeline assets
  2. Leverage emerging scientific and technological developments to generate new research ideas, solutions and initiatives using real-world data
  3. End-to-end experience in RWE studies including conceptualizing the research questions, data feasibility, study design, analysis, programming, and interpretation
  4. Analyze and interpret data to support urgent requests from internal and external stakeholders
  5. Ensure quality of design, execution, and publication of real-world evidence studies, and quality of models & tools

Skills

Required

  • Ph.D. degree, or master’s degree in a quantitative field (e.g., biostatistics, AI Data Science, Bioinformatics, similar)
  • Relevant experience (2+ years for Ph.D., 4+ years for a master’s) within biopharma companies, RWE consulting firms, or other relevant healthcare industries
  • Experience designing and executing studies using real-world healthcare data, such as claims, electronic health records (EHRs), or registries
  • Extensive hands-on experience with data engineering and data analysis
  • Proven track record of consistently delivering on high impact data science projects
  • Expert proficiency in either R or Python, SPARQL, Cypher
  • Experience delivering on Data Science projects using predictive technologies as machine learning/deep learning, or forecasting
  • Familiarity with and exposure to drug discovery and clinical development processes with one or more of the following therapeutic areas: oncology, immunology, neuroscience, or specialty ophthalmology
  • Hands-on experience with graph neural networks and related graph ML methods for learning across large, heterogeneous, multi-domain graphs.
  • Hand-on bioinformatics experience running and interpreting results in a biological/disease context, including pathway or functional enrichment analysis.

Nice to have

  • Deep neuroscience domain expertise: in-depth knowledge of neurodegenerative, neuropsychiatric and ophthalmic diseases, clinical development, and trial design challenges
  • Experience in multi-omics including designing and interpreting large-scale proteomic studies, analyzing data from large biobanks to identify causal drivers and clinical relevancy
  • Experience integrating multi-modal data for holistic disease understanding and patient subtyping
  • Advanced Analytics
  • Coaching
  • Critical Thinking
  • Data Analysis
  • Data Privacy Standards
  • Data Quality
  • Data Reporting
  • Data Savvy
  • Data Science
  • Data Visualization
  • Digital Fluency
  • Econometric Models
  • Organizing
  • Process Improvements
  • Strategic Thinking
  • Technical Credibility
  • Workflow Analysis

What the JD emphasized

  • Hands-on experience with graph neural networks and related graph ML methods for learning across large, heterogeneous, multi-domain graphs.
  • Hand-on bioinformatics experience running and interpreting results in a biological/disease context, including pathway or functional enrichment analysis.

Other signals

  • Leverage emerging scientific and technological developments to generate new research ideas, solutions and initiatives using real-world data
  • End-to-end experience in RWE studies including conceptualizing the research questions, data feasibility, study design, analysis, programming, and interpretation
  • Ensure RWE generation aligned with regulatory requirements and scientific standards
  • Experience delivering on Data Science projects using predictive technologies as machine learning/deep learning, or forecasting
  • Hands-on experience with graph neural networks and related graph ML methods for learning across large, heterogeneous, multi-domain graphs.