Computational Research Associate

Neptunebio; New York (NY); On-site; USD 70,000 - 90,000; Full time

Job summary

Neptunebio in New York is looking for a Computational Research Associate to enhance data analysis in functional genomics. You will process large biological datasets, optimize analysis workflows, and support data management.

Applicants should possess a B.S. or M.S. in a quantitative field, with at least 2 years of experience in biological data handling, proficiency in Python and/or R, and familiarity with cloud platforms like AWS or GCP.

Qualifications

• 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 working with cloud compute platforms (AWS, GCP, or similar).
• Strong organizational skills and attention to detail.

Responsibilities

• Process and analyze large-scale single-cell and perturb-seq datasets.
• Develop, document, and maintain reproducible analysis workflows.
• Support data management and organization across multiple datasets.
• Collaborate with scientists to translate raw data into results.
• Implement and optimize pipelines in cloud environments.
• Ensure reproducibility and traceability of analyses.

Skills

Python

R

Unix/Linux

AWS

GCP

Data Analysis

Data Visualization

Workflows Management

Education

B.S. or M.S. in Bioinformatics, Computational Biology, Computer Science, or a related field

Tools

Docker

Nextflow

Snakemake

Job description

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.