# Sr Scientist Biostatistics at Johnson & Johnson

Johnson & Johnson is hiring a Sr Scientist Biostatistics in Antwerp, Belgium. Level rates it Builds AI ●●●●; you can [apply on Johnson & Johnson](https://jobsbylevel.com/go/ae9b2740-9ce8-4829-9c65-910f40ea4622?utm_medium=ai-agent).

AI Level 4, AI centrality 94 out of 100. Beerse, Antwerp, Belgium.

## Details

- Company: [Johnson & Johnson](https://jobsbylevel.com/companies/johnson-johnson)
- AI level: AI Level 4 (score 94 out of 100)
- Location: Beerse, Antwerp, Belgium
- Posted: October 21, 2026
- Apply: https://jobsbylevel.com/go/ae9b2740-9ce8-4829-9c65-910f40ea4622?utm_medium=ai-agent

## Prepare for this job

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## Description

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: Data Analytics & Computational Sciences Job Sub Function: Biostatistics Job Category: Scientific/Technology All Job Posting Locations: Beerse, Antwerp, Belgium, Leiden, Netherlands, Maidenhead, Berkshire, United Kingdom, Prague, Czechia, Raritan, New Jersey, United States of America, Spring House, Pennsylvania, United States of America Job Description: Senior Scientist in AI and Biostatistics Role purpose The Discovery and Manufacturing Statistics organization within Johnson & Johnson is seeking a biostatistician with strong training and demonstrated applied experience in AI. The successful candidate will combine rigorous statistical thinking with expertise in modern AI technologies in order to develop trustworthy, validated and reusable AI-enabled statistical solutions for pharmaceutical research and manufacturing. The individual will work at the intersection of biostatistics, artificial intelligence and scientific software development and will ensure that AI solutions are not only technically scalable, but also statistically sound, scientifically interpretable and appropriate for decision-making in a regulated and validation-sensitive environment. Working with domain experts and technology teams, the successful candidate will combine hands-on development with large language models (LLMs), retrieval-augmented generation (RAG) and agentic workflows. The role supports the integration of scientific intelligence into evolving digital and laboratory environments, with reproducibility, traceability and expert oversight. Locations: Beerse (Belgium), Leiden (the Netherlands), Prague (Czechia), Maidenhead (UK), Spring House (PA), Raritan (NJ), Titusville (PA) Key responsibilities · Make statistical, mechanistic and predictive models accessible to AI workflows through documented interfaces and shared model services. Build Python services and connect approved R and Python tools through APIs and Model Context Protocol (MCP). · Be accountable for the statistical integrity of AI-enabled solutions, ensuring appropriate treatment of variability, uncertainty, confounding factors and generalisability of findings. · Design, develop and deploy AI-enabled scientific applications that integrate statistical methods, machine learning, generative AI and domain knowledge into scalable and reusable solutions. · Develop agentic workflows that retrieve evidence, invoke scientific models and support interpretation and proposed next experiments. Incorporate controlled tool execution, error handling and human review. · Prepare, curate and contextualise scientific data, experimental results and documents for AI applications. Ensure appropriate treatment of data quality, metadata, provenance and uncertainty, and contribute to knowledge-graph and semantic-integration initiatives where relevant. · Collaborate with modelling and laboratory automation teams on iterative workflows that connect experimental results, model updates and experimental design while maintaining scientific rigour, traceability and governance. · Work with platform and security teams to deploy and operate applications, including access controls, configuration, logging,

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