Computational Scientist Location:

1 DNA Way, South San Francisco, CA 94080

Contract Duration:

12 Months —

Potential Extension or Full-Time Conversion Pay Rate:

$35-$54.95/hour +

Benefits Benefits:

Weekly Pay, Medical, Dental, and Vision Insurance Position Summary The Human Genetics Department at our client is seeking a highly independent Computational Scientist with strong hands-on experience in genetic epidemiology, statistical genetics, computational biology, or bioinformatics . The selected candidate will develop and apply analytical approaches to integrate and interpret genetic, genomic, and clinical data . The role will focus on combining multiple sources of human biological data, including whole-genome sequencing (WGS), single-cell RNA sequencing (scRNA-Seq), single-cell ATAC sequencing (scATAC-Seq), and other multimodal datasets . This is a full-time 12-month contract position with potential for extension. The position may be hybrid or remote , ideally aligned with a U.S.-friendly time zone. Key Responsibilities Collaborate with Human Genetics scientists to analyze large-scale genetic, genomic, and clinical datasets from: Internal research studies and clinical trials High-throughput screening programs Academic and industry collaborations Publicly available datasets Develop analytical approaches to integrate and interpret complex biological datasets and generate insights into disease biology and translational research . Implement and apply machine learning algorithms to identify associations between imaging and omics data. Coordinate the intake, preparation, quality control, and organization of new datasets for analysis. Document analytical processes, methodologies, findings, and code. Present analytical findings to the Human Genetics department and cross-functional collaborators. Contribute to scientific publications and research projects. Required Qualifications Extensive experience with large-scale genetic and genomic data analysis . Strong understanding and experience in one or more of the following areas: Genetic epidemiology Statistical genetics GWAS and association analysis using array- and sequence-based genetic data Human genetic data analysis RNA-Seq and differential gene expression analysis Single-cell sequencing, including scRNA-Seq and/or scATAC-Seq Proteomics data analysis Integration of genetic and molecular data for multimodal analyses PhD in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology , or a related field; alternatively, a Master's degree with significant relevant experience.

Strong programming skills in:

R Python Shell scripting Experience with Git and high-performance computing environments, such as SLURM . Strong analytical and problem-solving skills with the ability to work independently. Curiosity and willingness to learn more about human genetics, bioinformatics, and biology. Ability to deliver high-quality analytical results with minimal supervision while meeting deadlines and making sound independent decisions. Strong written and verbal communication skills and the ability to collaborate effectively with cross-functional teams. Preferred Qualifications Familiarity with C++ . Experience working with emerging multimodal data integration methods . Experience integrating genomic, transcriptomic, clinical, and imaging datasets . Experience applying machine learning to biological or biomedical datasets. Experience contributing to scientific publications or translational research. #TMRD