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REQUIRED QUALIFICATIONS:

• PhD in Bioinformatics, Computational Biology, Physics, Computer Science or related field.
• Ability to define and solve logical problems for highly technical applications.
• Deep understanding of next!-generation sequencing data and bioinformatics algorithms. While Illumina sequencers are the most common data source, it is not unusual to see 10xGenomics or PacBio data.
• Provide best-!practices data analysis, including QC and visualization, employing common NGS pipelines such as RNA!seq, ChIP!-seq, ATAC-seq, Methyl-seq, DNA!-seq, and Microbiome Sequencing. Multi!omics analysis is often needed.
• Proven experience working in a Linux cluster environment. Experience in cloud environments is also a plus.
• Ability to select, adapt, and effectively use a variety of programming methods. Strong programming skills in Python and R are expected. Familiarity with C, C++, Java. and Perl are useful. Best practices in coding, including techniques of reproducible and transparent science (github, DockerHub, markdown documentation) will be required.
• Excellent verbal and written communication skills with both technical and non!technical clients.
• Stanford offers a supporting learning environment and ability to learn rapidly is a critical need. Our service supports a large and growing customer base and Stanford researchers are engaged in innovative basic science research. We expect our team members to ramp up rapidly to support this innovation.
• Strong work ethics are critical in this role, since the individual will be responsible for the highest quality scientific data analysis.

DESIRED QUALIFICATIONS:

• Postdoctoral training in one of the disciplines listed above is highly desirable for this position.
• While a wide background in NGS bioinformatics will be useful, deep experience with DNA!-seq and/or RNA-seq data is strongly preferred, including whole genome, whole exome, SNP!-array, tumor/!normal.
• Strong background in one or more of the following: pedigree analysis, machine learning, statistical methods, variant annotation and interpretation, Genome Wide Association Studies (GWAS).
• Knowledge of general principles for regulatory compliance, including understanding of the NIH Security Best Practices for Controlled !Access Data Subject to the NIH Genomic Data Sharing (GDS) Policy (aka dbGaP compliance).
• The core team has developed extensive set of data analysis pipelines on the local cluster and Cloud. We expect reuse of these pipelines. Candidate is also expected to add to this analysis library by developing custom/novel pipelines.
• Experience working in academic environments.
• Experience with mentorship or project management.

Contact information:
Please direct all applicants to http://stanfordcareers.stanford.edu/

Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford welcomes applications from all who would bring additional dimensions to the University's research, teaching and clinical missions.

As an organization that receives federal funding, Stanford University has a COVID-19 vaccination requirement that will apply to all university employees, including those working remotely in the United States and applicable subcontractors. To learn more about COVID policies and guidelines for Stanford University Staff, please visit https://cardinalatwork.stanford.edu/working-stanford/covid-19/interim-policies/covid-19-surveillance-testing-policy