Scientist , Single-Cell Genomics

Scientist I, Single-Cell Genomics

The goal of Allen Institute for Cell Science is to develop a comprehensive approach to measure, describe, and model cell states and their dynamic changes over time with the ultimate goal of uncovering the fundamental principles of multiscale, multicellular morphogenesis, including how groups of cells organize and achieve collective behaviors essential for life. Our approach encompasses multi-modal data collection including live 3D timelapse imaging, data analysis, theory, and predictions to understand cell states and cell state transitions in human induced pluripotent stem cell models.

As a division within the Allen Institute, the Allen Institute for Cell Science uses a team-oriented approach, focusing on accelerating foundational research, developing standards and models, and cultivating new ideas to make a transformational impact on science.

The Allen Institute for Cell Science seeks a collaborative Scientist I to support computational analysis of single-cell RNA-seq datasets in projects focused on cell-state transitions during tissue morphogenesis in human iPSC-derived 3D culture systems. This role is well suited to a scientist with strong biological grounding, quantitative skills, and an interest in using data analysis to generate clear biological insight.

The Scientist I will primarily analyze single-cell RNA-seq datasets, develop reproducible workflows and visualizations, and collaborate closely with experimental and computational colleagues to support biological discovery in human iPSC-derived 3D culture systems.

We believe that science is better when it includes different perspectives and voices. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.

We are an equal-opportunity employer and strongly encourage people from all backgrounds to apply for our open positions. Please submit a resume and cover letter to be considered for this role.

• Perform analysis of single-cell RNA-seq datasets to characterize cell states, state transitions, and biological heterogeneity in human iPSC-derived 3D culture systems
• Develop reproducible analysis workflows, code, and visualizations using sound data-analysis practices
• Build and maintain R Shiny applications and related outputs to support data exploration and internal scientific decision-making
• Collaborate with experimental and computational colleagues to interpret biological findings from data and align analysis plans with project goals, sample design, and metadata needs
• Support data organization, documentation, and version control to enable effective collaboration and reuse
• Stay current with methods and literature in single-cell genomics
• Communicate findings clearly through presentations, figures, documentation, and manuscript contributions

• PhD in cell biology, developmental biology, stem cell biology, computational biology, bioengineering, biophysics, or a related field, or equivalent combination of education and experience
• Experience analyzing single-cell RNA-seq datasets
• Proficiency in R and/or Python for biological data analysis
• Experience developing reproducible analysis workflows and clear data visualizations

• Proficiency in Linux-based computing environments and version control tools such as Git
• Ability to build interactive data-exploration applications, such as R Shiny apps
• Background in developmental biology, stem cell biology, morphogenesis, or cell-state transitions
• Experience working with iPSC-derived model systems or 3D cell culture systems
• Understanding of wet-lab assay design considerations and practical limitations of single-cell or spatial experiments
• Knowledge of spatial biology approaches, including spatial transcriptomics and/or multiplexed protein imaging
• Ability to work effectively on interdisciplinary projects with experimental and computational collaborators
• Strong organizational, written, and verbal communication skills
• Publication record demonstrating contribution to single-cell or related biological data analysis

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