Job Title: Computational Scientist - Immuno-Oncology
Location: South San Francisco, CA (Remote)
Pay: $50-63/hr
Position Type: Full-time contract for an initial period of one year, with the possibility of extension. The role is available as either a hybrid or fully remote position.
Our Client: A leading biotech company focused on immuno-oncology research.
Job Summary:
Our client is looking for a self-motivated computational scientist with a robust analytical background in statistical genetics or computational biology. The successful candidate will be responsible for developing and applying innovative analytical methods to integrate genetic variant analysis with single-cell sequencing data.
Key Responsibilities:
- Design and implement production-level pipelines to integrate data with single-cell RNA-Seq data.
- Collaborate with scientists in the genetics department to analyze large datasets, including genetic, genomic, and clinical data from internal studies, clinical trials, high-throughput screens, academic and industry collaborations, and publicly available datasets.
- Develop and refine analytical methods to integrate and interpret these diverse data types, providing insights into disease biology to support the company's translational research objectives.
Required Qualifications:
- PhD (or Master's degree with substantial experience) in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related field.
- Extensive experience in large-scale genetic and genomic data analysis, including:
- Expertise in analyzing single-cell RNA-Seq data and other single-cell sequencing techniques.
- Proficiency in integrating genetic and molecular data for multimodal analyses.
- Experience with association analysis using array-based and sequence-based genetic data.
- Strong programming skills in R, Python, and shell scripting.
This role offers the opportunity to contribute to cutting-edge research in immuno-oncology, working with a dynamic team to drive forward our understanding of disease mechanisms and potential therapeutic targets.