Sr Validation Engineer
Novo Nordisk3 days ago
London, United KingdomSenior
Responsibilities
- Design, execute, and document manual and automated tests that produce reproducible evidence of workflow and system performance.
- Investigate defects, distinguish software defects from model limitations or variability, and document technical findings.
- Build and maintain reusable test automation components, including fixtures, test-data generators, output checks, and reporting components.
- Integrate automated tests into CI/CD workflows and maintain test code under version control and peer review.
- Evaluate system architecture, integrations, cybersecurity, data integrity, execution environments, APIs, and data-processing workflows.
- Create validation documentation including design specifications, IT risk assessments, user requirements, and validation plans.
- Assess open-source packages and dependencies for intended use, security, licensing, maintenance, known defects, and community exposure.
- Test omics, imaging, and digital-device data workflows for input and output integrity, transformations, annotations, audit trails, and reproducibility.
- Evaluate AI models against intended use, reference datasets, metrics, acceptance criteria, bias, robustness, and human-interaction scenarios.
- Collaborate with data scientists, AI specialists, software developers, IT solution owners, platform teams, governance colleagues, and external partners.
Requirements
- Degree in computer science, data science, engineering, mathematics, or a related discipline, or equivalent professional experience.
- Practical experience with manual and automated testing, test design, debugging, and defect investigation using Python or similar.
- Experience with Git, code review, reusable test automation components, reproducible data analysis, and CI/CD workflows.
- Strong understanding of software architecture, APIs, integrations, execution environments, and data-processing workflows.
- Experience with Microsoft Azure and Databricks.
- Understanding of risk-based validation, data integrity, audit-ready evidence, Good Clinical Practice, and regulated clinical data science.
- Clear written documentation skills and an investigative approach to technical findings.
- Preferred experience with R, numerical or statistical testing, open-source security and licensing assessments, and AI evaluation.
- Motivation to build expertise in GCP-regulated clinical data science, AI evaluation, and emerging data modalities.
Benefits
- Opportunities for learning and development within a global healthcare company.
- Supportive collaboration with colleagues across science, development, validation, and quality.
- Occasional business travel, typically four to five times per year, with trips lasting approximately three to five days.
- Inclusive recruitment process and reasonable application adjustments for candidates with disabilities or neurodivergence.
- Application deadline: 11 May 2026; no cover letter is required, but applicants should include a few sentences about their motivation in their resume or CV.