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Part-Time Bioinformatics Scientist/Consultant
Job in
Pleasanton, Alameda County, California, 94566, USA
Listed on 2026-05-22
Listing for:
El Capitan Biosciences
Part Time, Contract
position Listed on 2026-05-22
Job specializations:
-
IT/Tech
Data Scientist, Machine Learning/ ML Engineer
Job Description & How to Apply Below
El Capitan Biosciences is a molecular diagnostics company developing a high-fidelity, non-invasive host RNA transcriptomics platform for gastrointestinal (GI) disease detection, monitoring, and companion diagnostics. Our proprietary stool RNA-seq technology enables ultra-sensitive detection of human transcripts in complex biological samples, combined with advanced machine learning to drive clinical insights.
Position OverviewWe 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.
Time Commitment- 10 - 20 hours per week (flexible schedule)
- Hourly rate: $50 - $85/hr
- 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
- 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
- Experience with machine learning frameworks (e.g., scikit-learn, Tensor Flow, 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
- 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
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