Bioinformatics Scientist (Hereditary Disease) job with Tempus Labs

We are looking for a bioinformatics scientist with experience and interest in translational genomic research, genetic analysis and/or algorithm/pipeline development. You will be working on analysis of genomics data and the development of new diagnostic products for common, hereditary diseases and will be based on the San Francisco Bay Area. This position requires experience with scientific programming and familiarity with common bioinformatics tools for NGS data analysis.


-Develop pipelines for the identification of genetic variant identification, analysis, and classification from next generation sequencing and clinical data.

-Collaborate with scientists, and clinicians to design and perform analyses on clinical sequencing data in order to improve quality of care.

-Develop algorithms pipelines to gain insight into genetic variation clinical impact through analysis and to enable and clinical diagnostics products.

-Communicate results and status with product and bioinformatics leadership.

-Produce high quality and detailed documentation for all projects.

Required Experience

-PhD in Computational Biology, Quantitative Genetics, Computer Science, Applied Mathematics, Genomics, Molecular Biology or related areas, or equivalent experience.

-Minimum of 3+ years of professional/postdoc experience, preferably in industry.

-Computational skills using Python (strongly preferred), Java, C/C++ or other programming languages, and databases (SQL).

-Hands-on development of bioinformatics analysis pipelines and workflow engines (e.g. SnakeMake, Nexflow).

-Experience in data analysis techniques and tools (e.g. R, SAS, Python notebooks, etc.).

-Good understanding of life sciences domain and omics technologies.

Ideal Candidates Will Possess

-Experience in quantitative genetics and/or human genomics.

-Experience with analyzing and processing next-generation sequencing data.

-Experience with communicating insights and presenting concepts to a diverse audience.

-Experience in implementing and parallelizing pipelines in cloud computing environments.

-Experience in utilizing large scale public or proprietary genomics and human genetics databases.

-Self-driven and works well in interdisciplinary teams.

-Track record of publications in related domains is a plus.

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