Research Data Analyst 2

Stanford University is hiring a Research Data Analyst 2 for the Julia Salzman lab in the Department of Biomedical Data Science. The role focuses on analyzing and integrating high-throughput genomic data, applying or developing algorithms, and creating reproducible analytical pipelines to address biological research questions.

Responsibilities

• Prioritize and extract data from a variety of sources such as notes, survey results, medical reports, and laboratory data, and maintain its accuracy and completeness
• Determine additional data collection and reporting requirements
• Design and customize reports based upon data in the database
• Oversee and monitor regulatory compliance for utilization of the data
• Use system reports and analyses to identify potentially problematic data, make corrections, and eliminate root cause for data problems or justify solutions to be implemented by others
• Create complex charts and databases, perform statistical analyses, and develop graphs and tables for publication and presentation
• Serve as a resource for non-routine inquiries such as requests for statistics or surveys
• Test prototype software and participate in approval and release process for new software
• Other duties may also be assigned

Skills

• Proficiency in high-throughput genomic data analysis and a good understanding of biological systems are required
• Experience in programming
• A solid background in statistical analysis
• Excellent interpersonal skills
• The ability to interact effectively with members of the research teams
• The successful candidate must be able to learn and work independently, yet collaborate effectively with co-workers
• 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
• Substantial experience with shell, command line and analytical programs
• Excellent writing and analytical skills
• Ability to prioritize workload
• Four-year college degree in bioinformatics, biostatistics, computational biology, computer science, or similar, with a strong publication record
• Alternatively, a PhD in one of the aforementioned fields (or similar) combined with a very strong record of data analysis
• Working knowledge and a solid understanding of biological principles, metagenomics, bioinformatics terminology, and common sequence alignment programs (eg. Bowtie)
• An understanding of the statistical principles behind current best practices in high-throughput molecular data analysis
• Strong experience in the use of a high-level programming language such as R, MATLAB, Python or Perl for complex data analysis
• Ability to generate novel pipelines to analyze unique types of data
• Proficiency with Unix systems and queuing systems (e.g. SLURM)
• Familiarity with reproducible computing research paradigms including Docker

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