Senior Principal Data Scientist, Real World Evidence (RWE)

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions 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 https://www.jnj.com

Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science

Job Category:

Scientific/Technology

All Job Posting Locations:

Cambridge, Massachusetts, United States of America, San Diego, California, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

Job Description:

Johnson & Johnson Innovative Medicine R&D Data Science and Digital Health is recruiting for a Senior Principal Data Scientist, Real World Evidence (RWE).

The primary location for this position is flexible – Titusville, NJ; Raritan, NJ; Spring House, PA; Cambridge, MA; or San Diego, CA. May consider remote on a case by case basis

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 https://innovativemedicine.jnj.com/

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 clinical programs and regulatory decision-making.   The Senior 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 accelerate clinical development in Oncology, Immunology, or Neuroscience.

Key Responsibilities:

• Be a hands-on technical leader, leading a portfolio of RWE projects while developing best practices and common technical tools

• Support the development of the R&D RWE strategy for the generation and utilization of RWE for our key pipeline assets

• End-to-end expertise in RWE studies (external control arm, novel endpoints, trial design optimization) including conceptualizing research questions, data feasibility, study design, analysis, programming, and interpretation

• Assess treatment patterns and unmet needs using RWE to inform asset positioning

• Ensure RWE generation aligned with regulatory requirements and scientific standards

• Identify evidence opportunities and develop innovative evidence programs to support early development, registration, and post-marketing phases

• Shape the selection of real-world data sources, establish data quality standards, and ensure data robustness and relevance for clinical research

• Lead and develop junior researchers and programmers in executing strategies to develop RWE from concept through publication

• Support regulatory interactions and meetings with scientific data packages and expertise

 

Required Qualifications:

• A Ph.D. degree, or master’s degree in a quantitative field (e.g., epidemiology, biostatistics, statistics, or similar)

• Relevant experience (4+ years for Ph.D., 6+ years for a master’s) within biopharma companies, RWE consulting firms, or other relevant healthcare industries

• Extensive hands-on experience with data engineering and exploratory data analysis, statistical modeling, time-to-event analyses, comparative effectiveness analyses, causal inference methods

• Demonstrated expertise with m