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# Part-Time Bioinformatics Scientist (Contract/Consultant)

- **Company:** Genegeniedx Corporation
- **Location:** Pleasanton, CA, United States
- **Salary:** $104,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Data Analysis, Bash Shell, Bioinformatics, Computational Biology, Computer Programming, Data Visualization, Python (Programming Language), Machine Learning, Tensorflow, High Performance Computing, Pytorch, Large Language Models, Deep Learning, Generative AI, Scikit Learn
- **Published:** May 15, 2026
- **Apply:** https://www.wayup.com/i-j-Part-Time-Bioinformatics-Scientist-Contract-Consultant-GeneGenieDx-975377352460456/

## About the Role

+ 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

## Description

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, + 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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