Computational Scientist - HUman Genetics

Computational Scientist - Human GeneticsOur client, a world leader in Biotechnology, is looking for a Computational Scientist - Human Genetics for South San Francisco, CA Job Duration: Long Term Contract (Possibility Of Extension) Pay Rate: $56/hr on W2 Company Benefits: Medical, Paid Sick leave, 401KThe Human Genetics department is seeking a highly independent Computational Scientist with hands-on experience in genetic epidemiology, statistical genetics, computational biology, or bioinformatics. The role will focus on developing and applying analytical approaches to integrate and interpret genetic, genomic, and clinical data, including large-scale sequencing and single-cell datasets. The scientist will contribute to multimodal data integration, machine learning, and translational research to generate insights into disease biology.Key Responsibilities:Analyze large-scale genetic, genomic, and clinical datasets from internal studies, clinical trials, high-throughput screens, academic collaborations, industry partners, and public datasets.Develop computational and statistical approaches to integrate and interpret complex biological datasets.Analyze whole genome sequencing, RNA-Seq, scRNA-Seq, scATAC-Seq, and other molecular assay data.Develop and apply multimodal data integration methods to connect genetic, molecular, clinical, and imaging data.Implement machine learning algorithms to identify associations between imaging and omics datasets.Coordinate the intake, preparation, quality control, and organization of new datasets.Document analytical workflows, code, methods, findings, and results.Present scientific findings to Human Genetics teams and cross-functional collaborators.Contribute to scientific publications and translational research initiatives.Required Qualifications:PhD, or Master's degree with significant relevant experience, in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related field.Extensive experience analyzing large-scale genetic/genomic datasets.Knowledge of genetic epidemiology and statistical genetics.Experience with GWAS and association analysis using array- or sequence-based human genetic data.Experience analyzing RNA-Seq, single-cell sequencing, and/or proteomic data.Experience integrating genetic and molecular datasets for multimodal analysis.Strong programming skills in R, Python, and shell scripting.Experience with Git and high-performance computing environments such as SLURM.C++ experience is a plus.Ability to work independently, make sound analytical decisions, meet deadlines, and produce high-quality results with minimal supervision.