Senior Scientist in AI and Biostatistics

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Senior Scientist in AI and Biostatistics

Locations

Antwerp, Belgium

Raritan, United States

Spring House, United States

Application Deadline

Application Deadline 28 days remaining

• 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

• 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:

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