Computational Scientist

Role: Computational Scientist

Location: South San Francisco, CA (Hybrid/Remote)

Duration: 12+ months

Overview: 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.



Must Have: Genetic Epidemiology, GWAS, RNA-Seq/scRNA-Seq/scATAC-Seq, Multimodal Data Integration, R/Python, High-Performance Computing (SLURM)



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 the data, delivering insights into disease biology to propel our translational goals
• Implement Client machine learning algorithms to understand associations between imaging and omics data
• 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:
  • Understanding of principles of genetic epidemiology
  • Association analysis with array- and sequence-based genetic data (GWAS - genome-wide association studies) on human 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