Stanford Center for Genomics and Personalized Medicine (SCGPM), situated in the heart of SF Bay Area, has an excellent new opportunity available for a motivated Research Engineer/Bioinformatician. This position is 1 year, fixed-term, renewable based on funding.
This is a key position with bioinformatics core team that will foster our multi-year collaboration with VA Palo Alto Health Care Systems (http://med.stanford.edu/gbsc/vapahcs.html). VA Palo Alto is now the new site for Epidemiological Research and Information Center (ERIC) for Genomics and is actively engaged in the Million Veteran Program (MVP) research. Our core team supports Prof. Philip Tsao, Director of ERIC for Genomics at VA and Professor of Cardiovascular Medicine at Stanford, and his collaborators within Stanford and VA. In his capacity at VA, Prof. Tsao is engaged in R&D with a goal to advance MVP objectives. This position will be deeply embedded with Prof Tsaos R&D team and may have dotted line reporting to Dr. Tsao or his senior staff members. As part of SCGPM core team, you will have access to other team members (http://med.stanford.edu/gbsc/scgpm-team.html) with expertise in complex data types (see http://med.stanford.edu/gbsc/baas.html). In terms of computational facility, the team has access to SCGPM bioinformatics facility which provides a best-in-class on-premise HPC cluster and fully integrated Google Cloud Platform.
Ideal person for this position is a Research Engineer/Bioinformatician who lives and breathes NGS data interpretation and is passionate about application of new analytical methods. The successful candidate will have deep knowledge of NGS technologies, quality analysis for read alignment and variant calling (secondary analysis), and a suite of analytical methods for discovering genetic components of human diseases (tertiary analysis). While the main focus includes DNA-Seq (whole genomes and exomes), the data may include RNA-Seq, SNP-array, Tumor-Seq and Methyl-Seq.
In this role, the team member will be fundamentally be involved in tertiary analysis and data interpretation. The data sets will include individuals (e.g. pilot projects using novel technologies such as 10xGenomics) and large cohorts (100s to 1000s to 10,000s). Therefore, expertise with individual sample analysis as well as cohort based approaches will be needed. Secondary analysis is expected to follow best practices for the data type. However, the tertiary analysis is expected to be challenging. The cohorts are from complex diseases and often tertiary analysis needs to explore a comprehensive panel of techniques including GWAS, pathway analysis and machine learning. Expertise in co-analysis of data with other datasets (ENCODE, GTEx) will be critical. At Stanford, you will also have access to orthogonal medical care datasets such as Optum, and Truven for phenotypic correlations in extended populations.
Responsibilities will include:
Staying abreast with best practices as well as cutting-edge data analysis methods from recent publications.
Be able to do quick proof-of-concept of new methods and protocols. Establishing and refining the process for bioinformatics analysis and validation. Providing scalable implementation of these methods for VA scale.
Conducting data analysis and interpretation that meets quality needs of peer review publication.
Providing VA collaborators with data, analysis protocols, figures, method descriptions, code, documentation and anything else that might be needed.
Presenting the bioinformatics efforts and best practices at conferences. Peer reviewed publications are required.
Managing time efficiently to support analysis needs of multiple datasets.
Provide training to VA collaborators and other SCGPM members so they can become proficient in analysis.
Meeting 3rd party developers, service providers and consortium members to stay abreast of new tools, solutions and practices in the field and bring suitable ones in-house.
Observing highest standards of customer interaction including maintaining confidentiality where needed.
Following compliance requirements will be critical since the datasets are considered highly sensitive.
Members of SCGPM team are expected to participate in activities that are important to the Center mission as well as collaborators mission including participation in conferences, arranging seminars, attending annual retreats and other academic events as necessary.
PhD or post-doctoral experience in Bioinformatics, Computational Biology, or related field.
Deep understanding of complex algorithms and their application to biological data will be needed.
Ability to define and solve logical problems for highly technical and often novel bioinformatics applications.
Proven experience working in a Linux cluster environment. Some solutions may need to be Cloud based.
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. Follow best practices in coding including reproducible and transparent science (github, dockerhub, markdown documentation) to support extended collaborative environment.
Deep understanding of sequencing data and bioinformatics algorithms. While Illumina NGS data is most common data type, it is not unusual to see 10xGenomics data or SNP-array data (Affymetrix and Illumina).
Provide best-practices data analysis and interpretation employing common NGS pipelines such as DNA-Seq (whole genomes, exomes), RNA-Seq, Tumor-Seq and Methyl-Seq, as well as SNP-array. Multi-omics analysis may be needed.
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.
Strong work ethics are critical in this role since the individual will be responsible for highest quality scientific data analysis for peer reviewed publication.
While a wide background in NGS bioinformatics will be useful, deep experience with DNA-Seq data is strongly preferred, including whole genome, whole exome, and tumor-normal.
Strong background in one or more of the following: pathway analysis, machine learning, statistical methods, variant annotation and interpretation, Genome Wide Association Studies (GWAS)
Knowledge of general principles for regulatory compliance including understanding of NIH Security Best Practices for Controlled-Access Data Subject to the NIH Genomic Data Sharing (GDS) Policy (aka dbGaP compliance) and HIPAA.
Experience working in academic environments. Prior experience with VA is a plus.
Mentorship or project management experience.
What to expect during the interview process:
Candidates must apply through Stanford career site. You may reach out to the hiring manager but your application can not proceed unless you apply directly via Stanford career site. SCGPM Bioinformatics team follows SF Bay Area industry standards when conducting interviews. A typical interview process consists of resume screening, phone screenings, coding challenge, on-site visit, reference and background checks.
SCGPM Bioinformatics Core team members are dedicated and respectful individuals and will make their best effort to process your application in time. It is not unusual for the process to take 3 months and we appreciate your patience. We encourage candidates to explore Stanford benefits on the web and if you have specific constraints or questions, please make sure that you mention those in your cover letter.
Stanford may be able to process a H1-B visa for qualified candidates. We expect the candidate to be able to start on OPT while the visa is in process. H1-B visas can take 3 or more months to process.
Please direct all applicants to http://stanfordcareers.stanford.edu/
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Stanford University is an equal opportunity employer and is committed to increasing the diversity of its faculty and academic staff. It welcomes nominations of and applications from women and members of minority groups, as well as others who would bring additional dimensions to the Universitys research, teaching and clinical missions."
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Stanford University is an equal opportunity employer and is committed to increasing the diversity of its faculty. It welcomes nominations of and applications from women, members of minority groups, protected veterans and individuals with disabilities, as well as from others who would bring additional dimensions to the university’s research, teaching and clinical missions.