Principal Scientist – Data Science (RWE) Full-time

Principal Scientist – Data Science, Real World Evidence (RWE)

Johnson & Johnson Innovative Medicine, R&D Data Science and Digital Health team is recruiting a Principal Scientist – Data Science, Real World Evidence (RWE). This position has a primary location of either Barcelona or Madrid, Spain. Hybrid position.

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.

The R&D Data Science RWE team within J&J Innovative Medicine develops innovative solutions leveraging a variety of different data sources across multiple disease areas in support of key clinical programs. Successful candidates will develop cutting-edge methodologies to develop RWD-based solutions to enable disease insights, improve patient outcomes, and enhance clinical development. The Principal Scientist, RWE, will work closely with strategic partners within Data Science and Digital Health and multi-disciplinary teams within J&J Innovative Medicine R&D to develop and deploy cutting-edge solutions to our clinical programs.

Key Responsibilities:

• Lead and contribute to the development of statistical/machine learning models in health and healthcare (e.g., disease identification, patient stratification, disease progression, clustering, simulations, forecasting) 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 (electronic health records, clinical development data, insurance claims, registries, others).
• Deliver scalable analytical/machine learning solutions and insights to impact functions and therapeutic areas within R&D and participate in cross-functional collaborations with internal scientific and data science teams, and external companies.
• Shape internal and external collaborations and define the scope of research questions.
• Closely partner with the Data Science Therapeutic Area (DSTA) and Therapeutic Area (TA) teams to execute on the priorities, building a roadmap to deliver the projects and present to senior cross-functional leaders.
• Be a hands-on technical leader among the Data Science team, helping institute best practices while crafting a data-driven culture, developing, and mentoring more junior members of the team while advocating for skill development, working alongside a robust team of data scientists, bringing fresh perspectives and scientific rigor across Data Science projects and therapeutic areas.
• Clearly articulate highly technical methods and results to diverse audiences and partners to drive decision-making.

Required qualifications:

• A Ph.D. degree, or master's degree in a quantitative field (e.g., statistics, biostatistics, epidemiology, applied mathematics, artificial intelligence, computer science, or similar).
• Relevant experience (2+ years for Ph.D., 4+ years for a master's) within a start-up, technology, or healthcare industry.
• Extensive experience with one of the following: statistical modeling, clustering and classification, causal inference methods, simulation, machine learning, deep learning.
• Hands-on technical data analysis and machine learning modeling experience (across roles).
• Proven project leadership in complicated context, able to influence and engage the strategic and technical partners in a matrix organization.
• Proven track record of consistently delivering on high impact data science projects.
• Expert proficiency in Python or R, and SQL.
• Excellent interpersonal, communication, and presentation skills.

Preferred qualifications:

• Experience delivering on RWE projects using advanced predictive methodologies including machine learning.
• In-depth expertise in at least one of the following domains: EHR, clinical development data, insurance claims, or registry data.
• 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.
• Experience working closely with healthcare subject matter experts.
• Ability to effectively communicate technical work to a wide audience.