Cell Painting Data Analyst

Requirements

• Educated to PhD level, with experience applying innovative data science and analytics in a biological context.
• Demonstrated experience with statistical methods relevant to feature extraction from images, normalization of phenotypic data, quality control, and multivariate analysis.
• Proficiency in data analysis and visualization with the ability to build and maintain reproducible data analysis workflows, with programming experience (e.g. Nextflow, Knime, R or Python).
• Knowledge of multi-dimensional data architecture, including metadata management, cross-system data integration, and efficient data retrieval across multiple sources.
• Strong problem‑solving skills with the ability to translate complex datasets into meaningful insights.
• Ability to present analytical findings to multidisciplinary scientific teams.
• Demonstrated technical leadership, with the ability to understand complex challenges, define a clear path forward and guide teams through technical challenges.
• Desirable Skills
• Cell biology knowledge and experience with Cell Painting assay workflows or high‑content imaging experimental setup.
• Familiarity with dimensionality reduction, clustering, batch correction methods, and visualization of large biological datasets.
• Experience with machine learning and computer visions approaches.
• Understanding of toxicology, pharmacology, or safety assessment.
• Application process
• Our site

Benefits

Additional Information

We have an exciting opportunity for a Cell Painting Data Analyst to join our Research Predictive Science team within Product Safety. In this role, you will apply advanced statistical and machine learning approaches to high‑content imaging data, contributing to innovative predictive safety strategies. Working at the interface of biology, imaging, and data science, you will translate complex cell painting datasets into insights that guide early research and safety decisions. Key responsibilities will include: Processing, curating and analyzing high‑dimensional Cell Painting datasets, ensuring data quality and consistency across experiments. Developing and applying statistical and exploratory data analysis approaches to extract meaningful morphological features and phenotypic signatures. Supporting, developing and implementing computational workflows for image‑based profiling, including feature extraction, normalization, and dimensionality reduction. Collaborating with biologists and toxicologists to interpret phenotypic patterns and explore how morphological signatures may relate to cellular processes relevant to safety assessment. Evaluating and comparing analytical methods, assessing performance, robustness, and suitability for Cell Painting applications. Documenting workflows, summarizing results, and communicate findings clearly to cross‑functional scientific teams. Supporting integration of approaches into other workflows and working with RDIT teams where required to achieve robust pipelines.

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