Computational Research Associate

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