Senior/Principal Scientist, Computational Biology

Location:

Boston, MA (onsite, full-time)

About the Role:

As a Senior Computational Biologist, your core job is to discover novel biological insight from rich biological data across transcriptomic datasets, including data from our own novel microfluidic-based assays and therapeutic programs, and to ground our AI and machine learning efforts in deep expertise in transcriptomics and biology. This role is for scientists who are genuinely excited about core research in developmental biology, stem cell biology, and AI, and who want to sit at the center of those questions. Responsibilities:

• Drive scientific interpretation of data from our novel microfluidic assays and therapeutic programs — single-cell transcriptomics, multi-modal readouts, and assay-specific signals (e.g. lineage inference, perturbation response,cell type and state annotation). - Work side-by-side with our ML team to ground foundation and predictive models in real biology — defining the right tasks, the right labels, and the right evaluations. - Develop statistically grounded methods for our novel assays and data, and contribute to the state of the art in AI-first computational biology. - Bring careful, detail-oriented analysis to the data — interrogating what it shows and how the assays, methods, and processing choices shape what we see. - Maintain depth across the modern methods landscape and choose or adapt and develop the right approach for each question. - Operate autonomously in a fast-moving startup, helping shape Cellular Intelligence’s computational biology roadmap. - Contribute technical guidance and knowledge-sharing across the team, collaborating closely with other comp bio, ML, and engineering colleagues. Qualifications:

• PhD in Computational Biology, Bioinformatics, Computer Science, or a related field. - 3+ years of experience analyzing large-scale genomic and transcriptomic datasets, such as single-cell RNA-seq and spatial omics. - Deep quantitative grounding — comfort with the statistics and probabilistic modeling underlying modern single-cell methods, and the judgment to reason about where they fail. - Deep fluency in the modern Python single-cell stack (scanpy, anndata, and adjacent tooling), comfortable customizing and extending it as the analysis demands. - A scientist’s instincts and rigor — curiosity about the biology, careful interpretation, and attentiveness to the methodological details that determine whether a finding holds. - Passion for data-driven biology and its potential to transform healthcare. for data-driven biology and its potential to transform healthcare

Preferred Qualifications:

• Strong domain knowledge in developmental biology or stem cell research (highly advantageous). - Experience interpreting and modeling data from non-standard or novel assays. - Experience collaborating closely with ML teams — defining tasks, labels, and evaluation frameworks for biological models. - Prior experience in a startup or fast-paced environment. - Strong publication record in top journals.