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Neptune Bio · 2 months ago

Computational Biologist

New York

Full-time

Onsite

Mid, Senior Level

3+ years exp

Neptune Bio is seeking a Computational Biologist who is passionate about using data-driven methods to reveal biological insights. The role involves designing, implementing, and scaling computational pipelines for single-cell datasets while collaborating with both experimental and computational scientists.

Biotechnology

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

PythonRUnix/LinuxSingle-cell analysisCloud computingStatisticsData modelingMachine learningData engineeringDeep learning frameworksCommunication skillsCollaboration

Required

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

Preferred

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