Computational Biologist

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.

• →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.

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.