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.
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