Sr Scientist Biostatistics

360Pharma; Beerse; On-site; EUR 90,000 - 130,000; Full time

Job summary

Johnson & Johnson seeks a biostatistician with AI expertise to develop and validate AI-enabled statistical solutions for pharmaceutical research and manufacturing. The role blends biostatistics with machine learning, scientific software development, and governance within a regulated environment.

You will build Python services, ensure statistical validity, and work on agentic workflows, LLMs, and RAG integration, partnering with domain and technology teams to deliver scalable, interpretable, and

Qualifications

• PhD or equivalent with strong training in statistics and data science.
• Experience applying statistical methods to AI-enabled solutions.
• Ability to communicate technical trade-offs in English and collaborate across teams.

Responsibilities

• Develop AI-enabled statistical solutions for pharmaceutical research and manufacturing.
• Ensure statistical integrity, handle uncertainty and generalisability in AI outputs.
• Design end-to-end architectures for production scientific AI applications.
• Collaborate to deploy and operate scalable, validated AI services and tools.

Skills

Biostatistics

Statistical modelling

Python

R

AI/ML experience

English communication

Education

PhD in Statistics/Biostatistics/Machine Learning/Data Science or related

Master’s degree with relevant experience

Tools

Python

R

APIs

SQL

Git

Job description

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.

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, monitoring and validation.
• Design end‑to‑end architectures for production scientific AI applications, covering user interfaces, services, APIs, data, models and deployment. Define integration patterns that fit shared enterprise platforms.
• Test applications and evaluate retrieval, tool use and generated outputs. Work with domain experts to assess scientific validity, uncertainty and limitations, and investigate failures.
• Contribute to the development of reusable statistical and AI capabilities across DMS, including documentation, software components, analytical frameworks, validation approaches, best practices and technical standards.
• Serve as a scientific and technical thought leader by educating and mentoring statisticians and scientists in the effective application of AI, machine learning and modern computational methods.

Required qualifications

• PhD in Statistics, Biostatistics, Machine Learning, Data Science, Computer Science, Artificial Intelligence or a related quantitative discipline, or a Master’s degree with commensurate relevant experience.
• Candidates whose primary degree is in Computer Science, AI or Software Engineering must demonstrate advanced knowledge and practical application of statistical modelling, study design, uncertainty quantification and model validation.
• Ability to communicate technical trade‑offs clearly in English, and collaborate with colleagues from different scientific and technical backgrounds.

Required statistical expertise

• Strong foundations in statistical inference and predictive modelling.
• Understanding of study and experimental design.
• Experience assessing uncertainty, bias, overfitting and generalizability.
• Experience with model validation, calibration and performance evaluation.
• Proficiency in R and/or Python for statistical analysis.
• Ability to explain complex statistical and AI concepts to scientific audiences.

Required AI and system design expertise

• Hands‑on experience developing AI or machine‑learning solutions.
• Experience with generative AI, large language models, retrieval‑augmented generation or agentic workflows.
• Demonstrated experience designing end‑to‑end architectures for production applications, including service boundaries, data flows, API contracts, security, scalability, reliability and observability, with the ability to explain architectural trade‑offs.
• Strong Python skills and familiarity with building maintainable APIs and services. Working knowledge of SQL, data pipelines and data‑quality controls.
• Experience building modular, tested and reproducible analytical software (Git).
• Understanding of AI evaluation, monitoring and responsible‑use considerations.
• Ability to connect AI components beyond the proof‑of‑concept stage to validated statistical or scientific functions.
• Experience moving AI or statistical solutions from prototype to a supported production application.

Preferred expertise

• Experience in one or more of the following areas would be valuable:
• Pharmaceutical, biotechnology, healthcare or other regulated or validation‑sensitive environments.
• Experience with manufacturing, discovery, omics or other complex scientific data.
• Experience with Bayesian modelling, design of experiments, mixed models, causal inference or high‑dimensional data.
• Scientific modelling, active learning or optimisation, particularly in iterative experimental workflows.
• RAG evaluation, hybrid retrieval, knowledge graphs and contextualisation of scientific data.
• MCP, agent orchestration, shared model services and workflows combining automated checks with expert review.
• R package development, Shiny, Quarto, interoperable R/Python workflows, FastAPI or modern web interfaces; familiarity with Posit Workbench or Posit Connect.
• Hosted or self‑hosted model APIs, LLM serving frameworks such as vLLM, and enterprise deployment environments.
• AI observability, access enforcement, prompt‑injection defenses and responsible handling of sensitive data.

Preferred Skills:

Advanced Analytics, Biostatistics, Clinical Trials, Collaboration, Consulting, Critical Thinking, Data Privacy Standards, Data Quality, Data Savvy, Digital Fluency, Good Clinical Practice (GCP), Quality Assurance (QA), Report Writing, Standard Operating Procedure (SOP), Statistical Analysis Systems (SAS) Programming, Statistics, Systems Development, Technologically Savvy