Computational Scientist - HUman Genetics

Our Client, a world leader in Biotechnology is looking for a "Computational Scientist - Human Genetics " for SSF, CA

Job Duration: Long Term Contract (Possibility Of Extension)

Pay Rate: $56/hr on W2

Company Benefits: Medical, Paid Sick leave, 401K

The Human Genetics department is seeking a highly independent Computational Scientist with hands-on experience ingenetic epidemiology, statistical genetics, computational biology, or bioinformatics. The role will focus on developing and applying analytical approaches to integrate and interpretgenetic, 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-scalegenetic, 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.
• Analyzewhole genome sequencing, RNA-Seq, scRNA-Seq, scATAC-Seq, and other molecular assay data.
• Develop and applymultimodal data integration methods to connect genetic, molecular, clinical, and imaging data.
• Implementmachine 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, inStatistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related field.
• Extensive experience analyzing large-scale genetic/genomic datasets.
• Knowledge ofgenetic epidemiology and statistical genetics.
• Experience withGWAS and association analysis using array- or sequence-based human genetic data.
• Experience analyzingRNA-Seq, single-cell sequencing, and/or proteomic data.
• Experience integrating genetic and molecular datasets formultimodal analysis.
• Strong programming skills inR, Python, and shell scripting.
• Experience withGit and high-performance computing environments such asSLURM.
• C++ experience is a plus.
• Ability to work independently, make sound analytical decisions, meet deadlines, and produce high-quality results with minimal supervision.

If interested, please send us your updated resume at

[email protected] / [email protected]