Jr Computational Biologist

We are seeking a highly motivated Computational Biologist to develop and evaluate computational approaches for analyzing single-cell transcriptomic data. This role focuses on quantifying phenotype activation, improving automated cell-state classification, and distinguishing true biological signals from stochastic noise. The ideal candidate thrives in dynamic research environments and is passionate about applying computational rigor to drug discovery challenges.

Key Responsibilities

• Systematically evaluate and compare computational approaches for quantifying phenotype activation across single-cell transcriptomes.
• Establish rigorous statistical baselines and develop negative control frameworks to improve the reliability of automated cell-state classification.
• Develop and implement novel computational methods to address limitations in existing analytical approaches.
• Design strategies to distinguish genuine biological signatures from stochastic noise in high-dimensional datasets.
• Summarize findings and present results in internal peer review meetings.
• Contribute to conference submissions and/or peer-reviewed journal manuscripts where appropriate.
• Ensure high standards of reproducibility, documentation, and code organization across all projects.

Required Qualifications

• Master’s degree with ongoing PhD pursuit or a recent PhD graduate in Computational Biology, Computer Science, Machine Learning, or a related field.
• Extensive hands-on experience in single-cell transcriptomics analysis using Scanpy, Ann Data, and Pandas.
• Strong proficiency in implementing machine learning and statistical models using scikit-learn and Sci Py.
• Demonstrated ability to write reproducible, well-documented, and organized code.
• Strong communication skills with the ability to clearly articulate complex computational challenges to scientific peers.
• Enthusiasm for drug discovery and comfort working in fast-paced, evolving research environments.

Additional Preferred Experience

• Background knowledge in cell biology and/or immunology.
• Experience in hypothesis testing, noise modeling, and benchmarking computational tools.
• Familiarity with Explainable AI (XAI) methods and/or large-scale biological dataset analysis.
• Proven ability to build, optimize, or extend novel bioinformatics pipelines.