AI/ML Summer Intern — Statistics & Decision Sciences

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.

Duration: 10–12 weeks (3–4 months)

Start: Summer 2026

Location: Multiple European locations; High Wycombe - UK, Issy-les-Moulineaux - FR, Allschwill - CH

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 across the globe. J&J Innovative Medicine Research & Development is recruiting AI/ML Summer Interns to join Statistics & Decision Sciences (SDS) and Manufacturing & Applied Statistics (MAS). You’ll partner with practicing statisticians, data scientists, and software engineers on real-world problems at the intersection of AI and pharmaceutical R&D/manufacturing—gaining hands-on experience and exposure to industry best practices.

What you’ll work on

During the internship you’ll be assigned to one or more of the following focus areas (we’ll match based on your skills and interests):

1) AI for Scientific & Process Modeling

• Design and prototype agentic AI workflows that discover, select, and fit mathematical models (e.g., dissolution profiles; broader process/kinetics use cases).

• Build and benchmark nonlinear curve-fitting and optimization routines; define quality/fit criteria and validation protocols.

• Generalize methods to additional pharma processes (stability modeling, process optimization, PK/PD signals).

• Package your work into reusable components and documentation for scientist end-users.

2) LLM Platform Integration for R Analytics

• Help enable secure, enterprise LLM capabilities for R-based statistical workflows by developing and testing OpenAI-compatible API endpoints for a self-hosted LLM stack.

• Implement and validate OpenAI-style /v1/chat/completions endpoints; support streaming and non-streaming modes.

• Add secure authentication, configuration for multiple models, and enterprise logging/guardrails.

• Create test suites and integration examples with R packages (e.g., ellmer, vitals); contribute to documentation and deployment guides; plan for future RAG/embedding integration.

Qualifications

Required

• Enrolled in an accredited European university (Bachelor’s, Master’s, or PhD) in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field; available full-time 10–12 weeks between June 1 and Sept 30, 2026.

• Excellent Python skills (scientific stack: NumPy/SciPy/pandas) and sound software development practices with Git.

• Solid grounding in statistical modeling, regression, and optimization; ability to analyze noisy experimental data.

• Experience with machine learning concepts and modern LLM usage patterns/APIs.

• Clear, proactive communicator; able to work independently and in cross-functional teams.

• Legally authorized to work in the hiring country without current or future visa sponsorship.

Preferred (nice to have)

• Experience with R and the analytical ecosystem (e.g., ellmer, testthat, shiny).

• Familiarity with OpenAI-compatible endpoints, FastAPI, microservices, and REST testing.

• Knowledge of vector databases, embeddings/RAG, and secure logging/guardrails in regulated settings.

• Exposure to Bayesian methods, uncertainty quantification, or PK/PD/process modeling.

• Cloud/containerization familiarity (AWS/Azure/GCP, Docker) for scalable deployments.

What you’ll gain

• Practical experience applying AI/ML to real pharmaceutical problems in R&D and manufacturing.

• Mentorship from senior statisticians/engineers and opportunities to present your work to stakeholders.

• A tangible portfolio: prototypes, APIs, tests, and documentation that can be adopted by end users.