Computational Scientist – Human Genetics

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

Our client is seeking a highly independent Computational Scientist to join the Human Genetics organization. This role will focus on developing and applying computational and statistical approaches to integrate and interpret genetic, genomic, and clinical data to generate insights into human disease biology.

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

You will collaborate closely with scientists and cross-functional teams to analyze data from internal studies, clinical trials, high-throughput screens, external datasets, and academic and industry collaborations.

Key Responsibilities

• Analyze large-scale genetic, genomic, and clinical datasets to support Human Genetics research.
• Develop computational and statistical approaches to integrate and interpret multiple biological data sources.
• Perform analyses involving GWAS, whole-genome sequencing, RNA-Seq, single-cell RNA-Seq/scATAC-Seq, and/or proteomic data.
• Develop and apply machine learning methods to investigate relationships between imaging and omics data.
• Support integration of genetic and molecular datasets for multimodal analyses.
• Coordinate data intake, preparation, quality control, and analysis workflows.
• Develop reproducible analytical pipelines and maintain well-documented code and processes.
• Communicate analytical results and biological insights to scientists and cross-functional collaborators.
• Present findings to internal teams and contribute to scientific publications.

Required Qualifications

• PhD in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related quantitative field; Master's degree with significant relevant experience may also be considered.
• Strong experience with large-scale genetic/genomic data analysis.
• Experience with GWAS/association analysis using array- or sequence-based human genetic data.
• Experience analyzing molecular assay data such as RNA-Seq, scRNA-Seq, scATAC-Seq, and/or proteomics.
• Experience integrating genetic, genomic, molecular, and/or clinical data for multimodal analysis.
• Strong programming skills in R and Python, plus shell scripting.
• Experience with Git/version control and high-performance computing environments, such as SLURM.
• Ability to work independently, make sound analytical decisions, meet deadlines, and communicate findings effectively.

Preferred Qualifications

• Experience with machine learning and multimodal data integration.
• Familiarity with C++.
• Experience working with human genetics and disease biology.
• Experience in pharmaceutical, biotechnology, academic research, or other biomedical research environments.