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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Position Summary

We are seeking a Computational Biologist who is passionate about using data-driven, scalable methods to reveal biological insights. The ideal candidate is an independent thinker with strong computational and quantitative skills, and the ability to collaborate closely with both experimental and computational scientists. You will design, implement, and scale computational pipelines for single-cell perturbation datasets, while contributing to model development and experimental design.

This is a unique opportunity to join a dynamic, interdisciplinary environment and help shape Neptune Bio’s computational strategy and infrastructure.

Key Responsibilities

• Develop, innovate, and maintain advanced computational methods to process, analyze, and interpret large-scale single-cell genomics and perturbation datasets.
• Collaborate with wet-lab and computational teams to integrate data from diverse experimental modalities and guide experimental design.
• Build, optimize, and scale data analysis pipelines using modern cloud computing environments (e.g., AWS, GCP, Azure).
• Contribute to Neptune Bio’s data infrastructure, ensuring reproducibility, scalability, and efficient access to large datasets.
• Stay current with advances in computational biology, machine learning, and scalable infrastructure, applying them to ongoing research challenges.
• Communicate findings clearly through reports, visualizations, and presentations to multidisciplinary audiences.

Qualification and Education Requirements

You must have:

• Ph.D. in Bioinformatics, Computational Biology, Computer Science, or a related quantitative field, OR equivalent experience (e.g., BS/MS with ≥3 years of relevant experience).
• Proficiency in Python, R, and Unix/Linux environments
• Demonstrated experience in single-cell or multi-omics data analysis.
• Solid understanding of statistics, data modeling, and modern machine learning approaches.
• Experience deploying and scaling computational pipelines on cloud platforms (AWS, GCP, or similar).
• Strong communication skills and enthusiasm for working in a collaborative, fast-paced environment.

Additional preferred experience includes:

• Background in functional genomics, CRISPR screens, or perturb-seq analysis.
• Experience integrating multi-source data to derive novel and impactful insights.
• Expertise in data engineering and reproducible research tools (e.g., Docker, Nextflow, Snakemake) as well as familiarity with cloud-native architectures and distributed compute.
• Strong publication record demonstrating innovation in computational methods or biological data analysis.
• Experience with deep learning frameworks such as PyTorch or TensorFlow.