Computational Scientist

Spectraforce Technologies, Inc.; South San Francisco (CA); Hybrid; USD 120,000 - 190,000; Full time

Job summary

Spectraforce Technologies, Inc. is seeking a highly independent Computational Scientist in Human Genetics for the gRED group in South San Francisco.

The role focuses on integrating genetic, genomic, and clinical data using advanced analytical methods to understand disease biology. Ideal candidates have deep expertise in statistical genetics and genomic data analysis, with strong programming skills in R and Python.

Qualifications

• Extensive experience in large-scale genetic/genomic data analysis with GWAS, RNA-Seq, or multi-omics.
• Strong foundations in genetic epidemiology and statistical genetics concepts.
• Proficient in R, Python, and shell scripting; familiarity with C++ is a plus.
• Able to work independently with minimal supervision and meet deadlines.

Responsibilities

• Collaborate to analyze large genetic/genomic/clinical datasets from internal studies and external partners.
• Develop and apply analytical approaches for multi-source data integration to reveal disease biology insights.
• Implement machine learning methods to link imaging and omics data.
• Prepare datasets for analysis and document methods and code.
• Present findings and contribute to publications.

Skills

Statistical genetics

Genetic epidemiology

Computational biology

R programming

Python programming

Shell scripting

C++ familiarity

Education

PhD in Statistical Genetics

Masters in a related field

Tools

Git

SLURM (HPC)

R

Python

Shell

C++

Job description

Position Title

Computational Scientist: Human Genetics, gRED

Work Location

South San Francisco, CA, 94080

Assignment Duration

12 Months (Possibility of extension)

Work Arrangement

Hybrid or remote

Position Summary

The Human Genetics department at our organization 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.

Key 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


• Implement novel 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

Qualification & Experience

• 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 (eg RNA-Seq) including differential expression methods, single-cell sequencing data (eg 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