Computational Scientist, Human Genetics & Genomics

Pharmaceutical Company; South San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Pharmaceutical Company in South San Francisco seeks a highly independent Computational Scientist to advance Human Genetics by integrating genetic, genomic, and clinical data to reveal insights into human disease biology.

The ideal candidate will have strong hands-on experience in statistical genetics, genetic epidemiology, computational biology, or bioinformatics, with experience analyzing large-scale genomic datasets and strong coding skills in R and Python.

Qualifications

• PhD in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology or related quantitative field.
• Experience with large-scale genetic/genomic data analysis.
• Experience with GWAS/association analysis using array- or sequence-based data.
• Experience analyzing RNA-Seq, scRNA-Seq, scATAC-Seq, and/or proteomics data.
• Strong programming skills in R and Python, plus shell scripting.

Responsibilities

• Analyze large-scale genetic, genomic, and clinical datasets to support Human Genetics research.
• Develop computational and statistical approaches to integrate and interpret multiple data sources.
• Perform analyses involving GWAS, whole-genome sequencing, RNA-Seq, single-cell RNA-Seq/scATAC-Seq, and/or proteomics.
• Develop and apply machine learning methods to multimodal analyses of imaging and omics data.
• Coordinate data intake, quality control, and analysis workflows; maintain reproducible pipelines.

Skills

Statistical Genetics

Computational Biology

Bioinformatics

R

Python

Shell scripting

Git

SLURM

Education

PhD in Statistical Genetics

PhD in Computational Biology

Master's with relevant experience

Tools

R

Python

SLURM

Git

Job description

Pharmaceutical Company in South San Francisco seeks a highly independent Computational Scientist to advance Human Genetics by integrating genetic, genomic, and clinical data to reveal insights into human disease biology.

The ideal candidate will have strong hands-on experience in statistical genetics, genetic epidemiology, computational biology, or bioinformatics, with experience analyzing large-scale genomic datasets and strong coding skills in R and Python.