R&D Data Science & Digital Health DSAI Intern Summer

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/.

Position Components:

In this role, you will work with colleagues in the JNJ Innovative Medicine R&D Data Science & Digital Health organization and partner with key businesses across JNJ Innovative Medicine (formerly Janssen) to apply advanced data science and analytics to answer key research questions. As an intern, you will get the opportunity to work on ground breaking problems at the intersection of machine learning and drug discovery. You will be a member of a dynamic team comprising data scientists and subject matter experts to create and iterate on data science solutions. Projects include, but are not limited to, developing, and applying predictive and generative machine learning methods for molecular property prediction with the ultimate goal of drug discovery.

• Development and evaluation of predictive and generative machine learning algorithms to analyze molecular datasets for drug discovery.
• Collaborate closely with other machine Learning scientists, computational biologist and chemists to develop and complete a research project.
• Document and disseminate research findings internally and externally (e.g.- publishing in top-tier ML conferences/journals)

• Currently pursuing a PhD degree in Statistics, Biostatistics, Data Science, Computer Science, Electrical Engineering, Computational Biology, Biomedical Informatics, or related quantitative field.
• Strong working knowledge of machine learning with demonstrated research experience, as evidenced by publications, public code contributions, or equivalent.
• Proficiency in Python and hands-on experience with Deep Learning frameworks such as PyTorch or TensorFlow to tackle scientific problems.
• Strong technical communication and presentation skills.
• Ability to understand academic research papers.

• Extensive research experience working with sequence modelling (e.g.- with Large Language Models (LLMs)) and/or Graph Neural Networks (GNNs).
• Knowledge and interest in drug discovery and experience working with drug discovery datasets, including graph, sequence, and/or structure-based data.