Junior Computational Biologist (Remote)
Junior Computational Biologist (Remote) Laboratory South San Francisco, CA, US
- Added - 13/02/2026
Pay Rate Low: 30 | Pay Rate High: 34 A leading biotechnology research organization is seeking a Junior Computational Biologist to support efforts in refining how cellular states are quantified and validated! Title: Jr. Computational Biologist (Remote Contract) Location: Remote (Must be available during PST business hours) Compensation: $30–34/hour + benefits Contract Duration: 6–12+ months Job Duties: This project will focus on benchmarking functional scoring methodologies and improving interpretability of high-dimensional transcriptomic datasets. The selected candidate will contribute to distinguishing true biological signal from technical variation in large-scale single-cell atlases, directly enhancing the reliability of automated cell-state classification frameworks. Start Date: July 1, 2026
- Duration: Through December 18, 2026
- Commitment: Full-time (100%)
- Ideal Candidate: Upcoming June 2026 PhD graduate or recent PhD graduate
- Location: Onsite in South San Francisco, CA preferred; remote within the U.S. considered (must work PST hours)
- Visa Sponsorship: Not availabl
Key Responsibilities
- Systematically evaluate and benchmark computational approaches for quantifying phenotype activation across single-cell transcriptomic datasets.
- Establish rigorous statistical baselines and negative-control frameworks to improve the robustness of automated cell-state classification methods.
- Develop or refine computational methods to address limitations in current approaches.
- Design strategies to distinguish genuine biological signatures from stochastic or technical noise.
- Present findings in internal scientific reviews and contribute to potential conference abstracts or peer-reviewed publications.
Required Qualifications
- Extensive hands-on experience in single-cell data analysis using Scanpy, AnnData, and Pandas .
- Strong proficiency implementing statistical and machine learning models using scikit-learn and SciPy .
- Demonstrated commitment to reproducible research practices and well-organized code.
- Ability to clearly communicate complex computational concepts to interdisciplinary scientific teams.
- Master’s degree with ongoing PhD pursuit, or recent PhD graduate, in Computational Biology, Computer Science, Machine Learning, or related quantitative discipline.
- Interest in drug discovery and comfort working in dynamic, research-driven environments.
Preferred Qualifications
- Background knowledge in cell biology and/or immunology.
- Experience with hypothesis testing, noise modeling, and benchmarking computational tools.
- Familiarity with Explainable AI (XAI) approaches or large-scale biological datasets.
- Demonstrated ability to build or extend novel bioinformatics pipelines.INDBH#LI-MG1
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