Junior Computational Biologist

Job Description

Junior Computational Biologist | Astrix Technology

The Tone:
This is a contract role at Astrix Technology, located in South San Francisco, CA, with remote options within the U.S. requiring PST hours. This position supports a leading biotechnology research organization focused on refining how cellular states are quantified and validated. The role is critical for improving the interpretability of high-dimensional transcriptomic datasets and enhancing the reliability of automated cell-state classification frameworks.

The TL;DR
• Role: Contract
• Type: Full-time
• Location: Remote
• Pay: $30–$34 hourly
• Team: Laboratory Department
• Mission: Refine how cellular states are quantified and validated by benchmarking functional scoring methodologies and improving interpretability of high-dimensional transcriptomic datasets.
• Tech Stack: Scanpy, AnnData, Pandas, scikit-learn, SciPy

What You’ll Actually Do
• Evaluate: Systematically evaluate and benchmark computational approaches for quantifying phenotype activation across single-cell transcriptomic datasets.
• Establish: Establish rigorous statistical baselines and negative-control frameworks to improve the robustness of automated cell-state classification methods.
• Develop: Develop or refine computational methods to address limitations in current approaches for data analysis.
• Design: Design strategies to distinguish genuine biological signatures from stochastic or technical noise within single-cell atlases.
• Present: Present findings in internal scientific reviews and contribute to potential conference abstracts or peer-reviewed publications.

The Must-Haves
• Background: Early career professional, upcoming June 2026 PhD graduate or recent PhD graduate in Computational Biology, Computer Science, Machine Learning, or a related quantitative discipline.
• Experience: Extensive hands-on experience in single-cell data analysis using Scanpy, AnnData, and Pandas. Strong proficiency in implementing statistical and machine learning models using scikit-learn and SciPy. Demonstrated commitment to reproducible research practices and well-organized code.
• Skills: Single-cell data analysis, statistical modeling, machine learning, reproducible research, clear communication of complex computational concepts to interdisciplinary teams.
• Bonus: Background knowledge in cell biology and/or immunology. Experience with hypothesis testing, noise modeling, benchmarking computational tools. Familiarity with Explainable AI (XAI) approaches or large-scale biological datasets. Demonstrated ability to build or extend novel bioinformatics pipelines.