Company Description
Located in the San Francisco Bay Area, El Capitan Biosciences comprises a team of passionate, versatile, and seasoned professionals. Our team's extensive research and industry backgrounds enable us to stay at the forefront of innovation.
At El Capitan Biosciences (ECB), our mission is to advance noninvasive diagnostics and monitoring by harnessing advanced machine learning algorithms and proprietary molecular methods to extract, quantify and interpret human RNA present in stool. By enabling whole transcriptome-sequencing of human transcripts shed from gut and immune cell populations, we aim to discover clinically meaningful biomarkers that reflect mucosal integrity, presence of disease, inflammatory activity and broader gastrointestinal health. Our multiplex RNA-based panels integrate signals across multiple pathways to deliver a comprehensive, actionable picture of disease biology, supporting improved diagnosis, monitoring, and overall understanding of gut health.
Company website: https://elcapitanbio.com/
Why Join Us
- Opportunity to help shape the commercial direction of a novel platform technology
- Direct impact on partnerships with leading pharma and diagnostics organizations
- Collaborative, science-driven startup environment
Job Description
Position Overview
We are seeking a highly skilled Part-Time Bioinformatics Scientist to support RNA-seq data analysis and machine learning model development for biomarker discovery and clinical diagnostics. This is a flexible role ideal for individuals seeking to contribute to cutting-edge translational genomics projects on a part-time basis.
Key Responsibilities
- Analyze RNA-seq datasets, including preprocessing, QC, normalization, and differential expression analysis
- Develop and maintain bioinformatics pipelines for transcriptomic data
- Apply statistical and machine learning methods to identify diagnostic or predictive biomarkers
- Build and evaluate predictive models, including deep learning approaches
- Explore applications of large language models (LLMs) or multimodal AI in biological data analysis
- Collaborate with cross-functional teams to interpret results and guide experimental design
- Communicate findings through reports, visualizations, and presentations
Qualifications
Basic Qualifications
- PhD candidate or PhD degree in Bioinformatics, Computational Biology, Biology, or a related field
- Hands-on experience with RNA-seq data analysis
- Strong programming skills in R and/or Python
- Familiarity with Linux/Unix environments and proficiency in bash scripting
- Formal training in statistics and machine learning
Preferred Qualifications
- Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
- Experience developing or applying deep learning models
- Familiarity with large language models (LLMs) or generative AI applications in biology
- Experience with NGS pipelines and tools (e.g., STAR, Salmon, nf-core, etc.)
- Experience working in high-performance computing (HPC) or cloud environments
- Strong data visualization and communication skills
Additional Information
What You’ll Gain
- Opportunity to work on cutting-edge RNA-seq datasets from clinical samples
- Exposure to real-world biomarker discovery and diagnostic development
- Flexible, part-time engagement with meaningful scientific impact
- Collaboration with a highly experienced, multidisciplinary team
