Data Science & Process Modeling Intern
About this opportunity
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.
Job Function:
Career Programs
Job Sub Function:
Non-LDP Intern/Co-Op
Job Category:
Career Program
All Job Posting Locations:
Schaffhausen, Switzerland
Job Description:
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.
Key responsibilities:
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.
Responsibilities include:
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.
Qualification:
Enrolled in or recently graduated from a Master's program in Chemical Engineering, Biological Engineering, Chemistry, Physics, Mechanical Engineering, Pharmaceutical Sciences, Engineering field or other related fields required.
Experience with python-based data analytics.
Experience with modeling and programming (e.g., Python, MATLAB, R, gPROMS, Aspen Plus, or other relevant software)
Motivated, entrepreneurial approach
Good problem-solving skills
Excellent verbal and written communication skills
Required Skills:
Problem Solving, Process Modeling
Preferred Skills:
Python for Data Analysis
Job details
How this role compares
Computed from every other active Biostatistics & Data Science role in our database, not just this employer's listings.
We currently track 25 comparable Intern/Fellow/Postdoc Biostatistics & Data Science roles across 10 biopharma companies.
Salary context
6 of 25 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 25 comparable roles
Seniority mix
25 of 25 peers have a known seniority level
Therapeutic area mix
0 of 25 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
No peers with a known therapeutic area yet.
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.