Computational Biologist

About NewLimit NewLimit is a biotechnology company working to radically extend human healthspan. We’re developing medicines to treat age-related diseases by reprogramming the epigenome, a new therapeutic mechanism to restore regenerative potential in aged and diseased cells. We leverage functional genomics, pooled perturbation screening, and machine learning models to unravel the biology of epigenetic aging and disease using experiments of unprecedented scale. Description NewLimit is seeking a Computational Biologist to join our Predict team. In this role, you will develop, improve, and operate bioinformatics pipelines supporting large-scale single-cell perturbation screens. Success in this position requires a deep attention to methodological detail at every stage of the analysis, as well as careful measurement and evaluation of pipeline performance. You will work closely with experimentalists and computational scientists to maintain core infrastructure, assess emerging analysis tools, and build robust software solutions that improve efficiency, scalability, and reliability. As a member of our team, you will: Collaborate with Write, Read, and Predict teams to support the design, execution, and interpretation of experiments using single-cell and multi-omics data Contribute to the development and implementation of primary and secondary analysis software. Improve and run production pipelines for large-scale single-cell perturbation screens to ensure timely and reliable data delivery Contribute to the development of reproducible, well-documented computational workflows and internal best practices Develop microservice web applications and integrations that reduce wet lab manual effort and improve data flow between teams Requirements Bachelor’s, Master's, or PhD in Bioinformatics, Biostatistics, Computational Biology, Computer Science, or a related technical field Experience analyzing and interpreting single-cell or multi-omics datasets (e.g., scRNA-seq, multiome, scATAC-seq), with hands-on exposure to perturbation screens preferred Familiarity with core single-cell bioinformatics workflows, including demultiplexing, alignment, and quality control Strong software engi neering fundamentals and experience working with bioinformatics tools and pipelines Working knowledge of statistical and machine learning methods applied to biological data (e.g.