Computational Scientist: Human Genetics
2 weeks ago
BEPC is looking for a Computational Scientist: Human Genetics in South San Francisco, CA to join our fast-growing team of consultants
W2 Contract: 11+ months with opportunities for extensions based on business needs and performance.
Pay Range: $58.53-61.53hr./plus we offer Medical, Dental, Vision and Life Insurance benefits
Schedule: Hybrid/Remote Position
Job Summary:
The Human Genetics department is seeking a highly independent computational scientist with a strong hands-on analytical background in genetic epidemiology, statistical genetics, or computational biology, to develop and apply analytical approaches to integrate and interpret genetic, genomic, and clinical data. We are particularly interested in candidates with skill sets that position them to tackle the integration of multiple sources of human biological data, such as whole genome sequencing and single cell RNA-Seq/ATAC-Seq data, including knowledge of emerging multimodal data integration methods.
Responsibilities:
- Collaborate with scientists in Human Genetics department to analyze large datasets of genetic, genomic, and clinical data from internal studies (including our clinical trials and high throughput screens), collaborations with academic and industry partners, and public external data sets.
- Develop analytical approaches to integrate and interpret these data, delivering insights into disease biology to propel our translational goals.
- Coordinate the intake and preparation of new datasets as they become available for analysis.
- Document process, findings, and code.
- Present findings to the department and cross-functional collaborators and contribute to publications.
Requirements:
- Extensive experience in large-scale genetic/genomic data analysis including one or more of the following areas of expertise:
- Must have an understanding of principles of GWAS on human data.
- Association analysis with array-and sequence-based genetic data.
- Analysis of sequence-based molecular assay data (e.g. RNA-Seq) including differential expression methods, single-cell sequencing data (e.g. scRNA-Seq, scATAC-Seq) and/or proteomic data.
- Integration of genetic and molecular data for multimodal analyses.
- PhD (or Masters with significant experience) in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related field.
- Fluent in R, python, and shell scripting. Some familiarity with C++ will be a plus.
- Experience working with git and high-performance computing (e.g. the slurm scheduling manager).
- Curiosity and desire to learn more about human genetics, bioinformatics, and biology.
- Ability to produce high-quality analysis results with minimal supervision. This includes meeting key deadlines and making sensible independent decisions
- Good communication skills and experience working as part of a team.
USOPS
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