AIML Summer Intern — Statistics & Decision Sciences (SDS)

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are 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 more at

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AI/ML Summer Intern Statistics & Decision Sciences (SDS)

Duration: 1012 weeks (34 months)

Start: Summer 2026

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

Openings: Up to 2 positions

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 sciencebringing 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). Youll partner with practicing statisticians data scientists and software engineers on real-world problems at the intersection of AI and pharmaceutical R&D/manufacturinggaining hands-on experience and exposure to industry best practices.

What youll work on

During the internship youll be assigned to one or more of the following focus areas (well 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.

3) Evaluation Frameworks for Generative AI in Document Automation

• Develop a practical multi-method evaluation framework to assess the quality reliability and risks of generative AI solutions used for scientific and operational document automation (e.g. SAP DPS I2I ASAP).

• Translate research insightssuch as ISO/IEC 25023 quality characteristics and modern GenAI evaluation metrics (e.g. BERTScore FActScore BLEURT MAUVE)into a tailored framework for QSCP and SDS use cases.

• Define quality dimensions (e.g. factual accuracy relevance usability reliability) and establish metric thresholds weighting schemes and monitoring workflows.

• Build a prototype toolkit or app enabling automated and humanintheloop evaluation of generative outputs.

• Conduct a pilot study with real J&J document-generation systems and deliver recommendations for scalable trustworthy quality assessment practices.

Qualifications

Required

• Enrolled in an accredited European university (Bachelors Masters or PhD) in Computer Science Data Science Statistics Applied Mathematics or a related field; available full-time 1012 weeks between June 1 and Sept 30 2026.

• Strong 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 youll 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.

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