Computational Research Associate

<p><strong>Position Summary</strong></p> <p>We are seeking a <strong>Computational Research Associate</strong> to support our data analysis and infrastructure efforts across functional genomics and single-cell perturbation experiments. The ideal candidate has hands-on experience working with large biological datasets, enjoys building efficient and reproducible analysis workflows, and thrives in a collaborative, fast-paced startup environment.</p> <p>You will play a central role in processing, analyzing, and organizing single-cell and perturb-seq data, maintaining and improving computational pipelines, and supporting the broader team with high-quality data outputs and infrastructure.</p> <p><strong>Key Responsibilities</strong></p> <ul> <li>Process and analyze large-scale single-cell and perturb-seq datasets using established computational pipelines.</li> <li>Develop, document, and maintain reproducible analysis workflows and data processing infrastructure.</li> <li>Support data management and organization across multiple internal and external datasets.</li> <li>Collaborate closely with experimental and computational scientists to translate raw data into interpretable biological results.</li> <li>Implement and optimize pipelines in cloud environments (e.g., AWS, GCP) for scalable data processing.</li> <li>Maintain codebases, perform quality control on data outputs, and ensure reproducibility and traceability of analyses.</li> <li>Generate clear reports, visualizations, and summaries to communicate results across teams.</li> </ul> <p><strong>Qualification and Education Requirements</strong></p> <p><em>You must have:</em></p> <ul> <li>B.S. or M.S. in Bioinformatics, Computational Biology, Computer Science, or a related quantitative field.</li> <li>2+ years of experience working with biological or single-cell datasets.</li> <li>Proficiency in Python and/or R for data analysis and visualization.</li> <li>Familiarity with standard genomics tools and file formats (FASTQ, BAM, HDF5, AnnData, etc.).</li> <li>Experience using and maintaining analysis pipelines in a Unix/Linux environment.</li> <li>Experience working with cloud compute platforms (AWS, GCP, or similar).</li> <li>Strong organizational skills, attention to detail, and commitment to clean, reproducible code.</li> </ul> <p><em>Additional preferred experience includes:</em></p> <ul> <li>Experience analyzing single-cell RNA-seq or perturb-seq datasets.</li> <li>Familiarity with workflow management systems (Nextflow, Snakemake, or similar).</li> <li>Experience with containerization tools such as Docker.</li> <li>Exposure to data engineering concepts (e.g., databases, versioned data storage, data pipelines).</li> <li>Understanding of basic statistical methods for genomics data analysis.</li> <li>&nbsp;</li> </ul>