Computational Scientist

The role will develop computational and statistical approaches to interpret human biological data, including whole genome sequencing, single-cell sequencing, imaging, and multimodal datasets. Responsibilities include developing analytical workflows, applying machine learning methods, preparing datasets, documenting analyses and code, and presenting scientific findings.

The Fountain Group is currently seeking a Computational Scientist  for a prominent client of ours. This position is located in Remote / Hybrid  (South San Francisco, CA). Details for the position are as follows:
 

Job Title: Computational Scientist – Human Genetics

Location: Remote / Hybrid – US-friendly time zone
Pay Rate: $25.23–$54.95/hr
Duration: 1 Year Contract

Job Description

Computational Scientist supporting Human Genetics research through analysis and integration of large-scale genetic, genomic, and clinical datasets. The role will develop computational and statistical approaches to interpret human biological data, including whole genome sequencing, single-cell sequencing, imaging, and multimodal datasets. Responsibilities include developing analytical workflows, applying machine learning methods, preparing datasets, documenting analyses and code, and presenting scientific findings.

Education

• PhD in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or related field.

• Master’s degree with significant relevant experience may be considered.

Required Skills

• Hands-on experience with large-scale genetic/genomic data analysis.

• Genetic epidemiology and statistical genetics principles.

• GWAS and association analysis using array- and sequence-based human genetic data.

• RNA-Seq analysis and differential gene expression.

• Single-cell data analysis, including scRNA-Seq and/or scATAC-Seq.

• Integration of genetic, genomic, molecular, and clinical data for multimodal analysis.

• R and Python programming.

• Shell scripting.

• Git version control.

• High-performance computing environments, including SLURM.

• Experience analyzing large biological datasets with minimal supervision.

Preferred Skills

• Whole genome sequencing (WGS) analysis.

• Proteomic data analysis.

• Multimodal data integration methods.

• Machine learning for imaging and omics data.

• Experience integrating imaging and molecular datasets.

• C++ familiarity.

• Experience with clinical trial, high-throughput screening, or public genomic datasets.

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