At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, andsolutions are personal.Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.Learn more at
Job FunctionData Analytics & Computational Sciences
Job Sub FunctionData Science
Job CategoryScientific/Technology
All Job Posting LocationsCornellà de Llobregat, Barcelona, Spain, Madrid, Spain
Job DescriptionJohnson & Johnson Innovative Medicine R&D; Data Science and Digital Health is recruiting for a Principal Data Scientist, Real World Evidence (RWE). J&J; Innovative Medicine develops treatments that improve the health of people worldwide. Research and development areas encompass oncology, immunology, neuroscience, cardiopulmonary and specialty ophthalmology. Our goal is to help people live longer, healthier lives. We have produced and marketed many first-in-class prescription medications and are poised to serve the broad needs of the healthcare market – from patients to practitioners and from clinics to hospitals. To learn more about Johnson & Johnson Innovative Medicine visit The R&D; Data Science & Digital Health, Real-World Evidence (RWE) team at J&J; Innovative Medicine is dedicated to developing innovative evidence solutions and critical insights through diverse data sources including RWD, trial data and emerging innovative data sources, to support our clinical programs and regulatory submissions. The Principal Data Scientist will collaborate closely with strategic partners in R&D; Data Science and Digital Health, as well as multidisciplinary teams within J&J; Innovative Medicine, to develop and implement evidence and insights to improve patient outcomes and enhance clinical development in Oncology, Immunology, or Neuroscience.
Key Responsibilities
Contribute to the development of a portfolio of RWE projects based on RWD that will provide key insights to our pipeline assets
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
Analyze and interpret data to support urgent requests from internal and external stakeholders
Ensure quality of design, execution, and publication of real-world evidence studies, and quality of models & tools
Create study protocols, statistical analysis plans, and statistical programming deliverables
Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making
Ensure RWE generation aligned with regulatory requirements and scientific standards
Required Qualifications
A Ph.D. degree, or master's degree in a quantitative field (e.g., epidemiology, biostatistics, statistics, Bioinformatics, or 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, as well as SQL
Excellent interpersonal, communication, and presentation skills
Preferred Qualifications
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
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