Data Science Graduate Intern, AI/ML (Causal Inference/ Bayesian Optimization)

R&D Data Science & Digital Health DSAI Intern Summer 2025

Caring for the world, one person at a time has inspired and united the people of Johnson & Johnson for over 130 years.

We embrace research and science -- bringing innovative ideas, products and services to advance the health and well-being of people.

Employees of the Johnson & Johnson Family of Companies work with partners in health care to touch the lives of over a billion people every day, throughout the world.

With $81.6 billion in 2018 sales, Johnson & Johnson is the world's most comprehensive and broadly based manufacturer of health care products, as well as a provider of related services, for the consumer, pharmaceutical, and medical devices and diagnostics markets.

There are more than 250 Johnson & Johnson operating companies employing approximately 130,000 people in 60 countries throughout the world.

Calling all big thinkers and world-changers who want to drive their careers forward across more than 250 leading businesses in consumer, pharmaceutical and medical technology.

If you have the talent and desire to touch the world, Johnson & Johnson has the career opportunities to help make it happen.

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 cutting-edge problems at the intersection of artificial intelligence 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 artificial intelligence methods for molecular property prediction and generation with the ultimate goal of drug discovery.

Interns could be located out of our US locations in San Diego, San Francisco, Titusville, Raritan, Spring House, Boston as well as remote.Minimum qualifications:


* Currently pursuing a PhD degree in Data Science, Computer Science, Electrical Engineering, Statistics, or related quantitative discipline.


* Strong working knowledge of machine learning with demonstrated research experience, as evidenced by publications, public code contributions, etc.


* 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 and write academic research papers.

Preferred qualifications:

Experience in one or more of the following:


* Extensive research experience in Causal Discovery/Inference/Causal Representation Learning.


* Familiarity with Bayesian experimental design and Bayesian Optimization is a plus.


* Experien...