Junior Computational Biologist; Remote

Junior Computational Biologist (Remote)

Location: Remote (Must be available during PST business hours)

Compensation: $30-34/hour + benefits

Contract Duration: 6-12+ months

Pay Rate: $30-34 per hour

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 Preference: Onsite in South San Francisco, CA preferred; remote within the U.S. considered (must work PST hours)

Visa Sponsorship: Not available

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.

• 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 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.

• Extensive hands‑on experience in single‑cell data analysis using Scanpy, Ann Data, and Pandas.
• Strong proficiency implementing statistical and machine learning models using scikit‑learn and Sci Py.
• 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.

• 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.

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.

If you have questions about this posting, please contact