Junior Computational Biologist

Job Description

Junior Computational Biologist (Remote)

A leading biotechnology research organization is seeking a Junior Computational Biologist to support efforts in refining how cellular states are quantified and validated!

About the Role

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.

Key Details

• Title: Jr. Computational Biologist (Remote Contract)
• Location: South San Francisco, CA (Onsite preferred); Remote within the U.S. considered (must work PST business hours).
• Compensation: $30-34/hour + benefits
• Contract Duration: 6-12+ months (Specific project duration: July 1, 2026 – December 18, 2026)
• Commitment: Full-time (100%)
• Start Date: July 1, 2026
• Ideal Candidate: Upcoming June 2026 PhD graduate or recent PhD graduate
• Visa Sponsorship: Not available

Job Duties

The core objective of this role is to enhance the robustness and interpretability of single-cell transcriptomic data analysis by:

• Benchmarking functional scoring methodologies.
• Improving the interpretability of high-dimensional transcriptomic datasets.
• Distinguishing true biological signal from technical variation in large-scale single-cell atlases.
• Directly enhancing the reliability of automated cell-state classification frameworks.

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.

Equal Opportunity Employer

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.