Stanford University is seeking a Bioinformatics Engineer to manage and analyze large amounts of information, typically technical or scientific in nature, independently with minimal supervision.
About the Department of Pathology
Comprised of extraordinary faculty and staff, our mission is to improve the ability to diagnose, treat and understand the origin and manifestation of human disease, and to care for those who have or are at risk to develop disease. We accomplish this through our clinical services (in all fields of anatomic and clinical pathology, including molecular and genomic pathology, histocompatibility testing and transfusion medicine) and be research (which includes basic, translational and clinical research into the origins and manifestations of disease, including efforts to improve disease prediction and prevention as part of the goal of achieving precision medicine and health), and also by educating future leaders in pathology and related fields. Everything we do is to achieve the goals of providing the highest quality of clinical services to the patients for whom we passionately care, to advance our ability to understand, diagnose, monitor and ultimately to cure disease or to prevent or delay its occurrence, and to provide outstanding education and career development opportunities to those who share these goals.
For more information about the department visit http://pathology.stanford.edu/
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.
About GREGoR Stanford Site :
GREGoR Stanford Site (GSS), is one of 6 sites in the National Institutes of Health (NIH)’s Genomics Research to Elucidate the Genetics of Rare disease (GREGoR) Consortium. The GSS (https://gregor.stanford.edu/) mission is to provide a platform for functional genomics research and validation to improve diagnosis in Mendelian disease through integrated analysis of multi-omics data. GSS is led by co-PIs Dr. Stephen Montgomery (Department of Pathology), Dr. Matthew Wheeler (Division of Cardiovascular Medicine) and Dr. Jon Bernstein (Division of Medical Genetics).
At GSS, research participants who remain undiagnosed after exome sequencing will undergo short read and long read genome sequencing, transcriptome sequencing, methylation assays, metabolomics and/or lipidomics assays. State-of-the art computational algorithms and new methods will be applied and developed to prioritize the variants and genes. The data will be analyzed in a secure and scalable manner using a HIPAA compliant cloud platform like AnVIL and Google Cloud Platform (GCP). Novel causal variants and genes will be validated through state-of-the-art targeted approaches including massively parallel reporter assays, induced-pluripotent stem cell assays and CRISPR engineered cellular and mouse models.
About the Position:
We are seeking a highly talented and motivated Bioinformatics Engineer to support our GSS pipeline and data analysis team. Your role will focus on the development of pipelines and tools for comprehensive analysis of large amounts of molecular data generated by the GSS, with special emphasis on genomics, transcriptomics, and metabolomics datasets. You will also establish and support cloud infrastructure for storage and computation of the multi-omics data. Your ability to understand biological experiments, strong experience in the analysis of large-scale biological datasets, proficiency with programming languages and experience in high-performance computing environments or cloud will be a key resource in enabling high-quality data to flow through our systems to enable diagnosis and discovery of new disease-gene associations. A complete application will include CV (including GitHub account) and cover letter.
Duties include:
- Development of pipelines and tools for the comprehensive analysis of large amounts of multi-omics data being generated by the GSS.
- Establish and maintain the cloud infrastructure for GSS on AnVIL and GCP.
- Interact with GREGoR team members as well as external tool developers to implement new tools, algorithms, and updates.
- Develop intuitive reports for molecular pipelines for tracking progress and quality metrics.
- Track reports for problems with pipeline analysis and underlying data.
- Extract relevant data from a variety of sources, including RESTful API services, databases and medical records.
- Serve as a resource for bioinformatics inquiries from the clinical team members to access data or results from local cluster/cloud/external provider.
- Work with stakeholders across the consortium to best understand data structures to model metadata schemas.
- Mentorship to Junior Analysts with regards to primary analysis
- Document and report as needed to fulfill grant and regulatory obligations.
- Other duties may also be assigned
Due to the nature of the work, this position will be fully onsite.
Desired Qualifications:
- Graduate degree (Ph.D or M.S) that emphasizes bio/medical informatics, engineering, computer science and statistics are preferred.
- Relevant work experience preferred, two or more years.
- Domain expertise in analysis and running pipelines and bioinformatic tools for at least one of the following ‘omes: genomics, transcriptomics, metabolomics, atac-seq, proteomics.
- Proficiency in Python and/or R and Linux bash scripting.
- Experience with pipeline languages like WDL or snakemake or nextflow.
- Proven track record of data and infrastructure management in a HPC (High Performance Computing) cluster or cloud computing like Google Cloud Platform or AWS.
- Experience with container systems such as setting up virtual machines and docker instances.
- Experience and knowledge of code management such as github.
- Experience in systems biology approaches for data integration is a plus
- Experience in developing tools and statistical methods for large-scale data analysis is a plus.
- Biological domain knowledge (rare disease) is a plus.
- Experience on bioinformatics and/or software development team-based projects.
- Willingness to work in a highly collaborative environment. Experience as part of large NIH consortiums is a plus.
- Ability to quickly adapt and learn the latest tools and skills.
- Strong communication skills (e.g., put together reports and presentations).
- Ability to work independently (e.g., find papers relevant to the subject, assess methods, implement methods, and apply them to datasets to reproduce results).
Education & Experience (REQUIRED):
Bachelor's degree and three years of relevant experience or combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering.
Knowledge, Skills and Abilities (REQUIRED):
- Substantial experience with MS Office and analytical programs.
- Excellent writing and analytical skills.
- Ability to prioritize workload.
Physical Requirements*:
- Sitting in place at computer for long periods of time with extensive keyboarding/dexterity.
- Occasionally use a telephone.
- Rarely writing by hand.
- Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.
The expected pay range for this position is $100,000 to $113,000 per annum. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.
How to apply? Thanks!
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