Jr. Computational Biologist

Job Description

Jr. Computational Biologist | Astrix Technology

The Tone:
This is a full-time contract role at Astrix Technology, with remote options available for candidates within the U.S. who can work during PST business hours, although onsite presence in South San Francisco, CA is preferred. Astrix Technology partners with a leading biotechnology research organization focused on advancing biological understanding. This role is crucial for refining how cellular states are quantified and validated, directly contributing to the reliability of automated cell-state classification frameworks.

The TL;DR
• Role: Contract
• Type: Full-time
• Location: Remote (must work PST business hours); Onsite in South San Francisco, CA preferred
• Pay: $30–$34 hourly
• Mission: This project will focus on 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.
• Design: Design strategies to distinguish genuine biological signatures from stochastic or technical noise.
• Present: Present findings in internal scientific reviews and contribute to potential conference abstracts or peer-reviewed publications.

The Must-Haves
• Background: Master’s degree with ongoing PhD pursuit, or recent PhD graduate, in Computational Biology, Computer Science, Machine Learning, or a related quantitative discipline. Early Career level.
• Experience: Extensive hands-on experience in single-cell data analysis. Demonstrated commitment to reproducible research practices and well-organized code.
• Skills: Strong proficiency in Scanpy, AnnData, and Pandas. Expertise in implementing statistical and machine learning models using scikit-learn and SciPy. Ability to clearly communicate complex computational concepts to interdisciplinary scientific teams. Interest in drug discovery and comfort working in research-driven environments.
• Bonus: 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.