Data Science & Process Modeling Intern

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 jnj.com.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

We are eager to bring on board a skilled and motivated Master's student (or recent Master's graduate) to our Process Science Modeling and Data team for 6 months. Our organization supports all sites, platforms and modalities - all over the globe.

Join a multidisciplinary team working at the intersection of data science, process modeling, and pharmaceutical manufacturing. You will contribute to projects that leverage manufacturing data and process models to improve process understanding, optimize operations, and support sustainable manufacturing initiatives such as solvent recovery. This internship offers hands-on experience with real industrial challenges while collaborating with scientists and engineers.

• →Data science tasks:
  • →Clean, preprocess, and analyze manufacturing process data from existing datasets.
  • →Apply exploratory data analysis and visualization techniques to uncover patterns and generate actionable insights.
• →Modeling and process simulation tasks:
  • →Support projects on distillation and solvent recovery process modeling.
  • →Apply novel process models to address manufacturing and process development needs, refining model components as needed to accurately represent the processes under study.
  • →Perform simulation and optimization studies to improve process performance.
  • →Present results and recommendations to scientific and engineering teams.