Research Data Analyst 1 (Job Number: 70840)
An exciting opportunity is available for a bioinformatician in the Department of Genetics at Stanford University. The position offers the opportunity to contribute to cutting edge research in the field of aging, in particular the transcriptional and epigenetic regulation of the aging process. In this position, you can expect to perform data analysis on high-throughput genome-wide datasets in a wide range of research projects and different organisms. The bioinformatician will work in a highly collaborative environment, carry out data analysis and integration, and apply best-in-class algorithms – or develop new algorithms – that directly address the motivating biological questions. Further, the bioinformatician will develop reproducible and well-documented analytical pipelines for the analysis of high-throughput data, such as single-cell RNA-seq. The successful candidate will support several research projects in the lab and will have the opportunity to conduct his/her own research project. Collaborations across other labs and across departments are encouraged.
Proficiency in high-throughput genomic data analysis and a good understanding of biological systems are required, as are experience in programming and a solid background in statistical analysis. Excellent interpersonal skills and the ability to interact effectively with members of the research teams are essential to the success of the individual in this position. The successful candidate must be able to learn and work independently, yet collaborate effectively with co-workers.
- Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.
- Develop algorithms and statistical models, and perform statistical analyses appropriate to data.
- Prepare figures for publication and presentation.
- Collaborate with lab members on data collection and analysis methods.
- Four-year college degree in bioinformatics, biostatistics, computational biology or similar, with strong publication record. Alternatively, a PhD in molecular biology, etc. combined with a very strong record of high-throughput data analysis, supported by publication in this area.
- A solid understanding of biological principles and genetics. Enthusiasm for learning more.
- Broad experience with data generated by one or more high-throughput molecular assays: (single-cell) RNA-seq, ChIP-seq, flow cytometry / CyTOF, mass spectrometry, etc.
- An understanding of the statistical principles behind current best practices in high-throughput molecular data analysis.
- Ability to generate novel pipelines to analyze unique types of data.
- Strong experience in the use of a high-level programming language such as R, MATLAB, Python or Perl for complex data analysis.
- Strong statistical background and experience in applying statistical packages in R and proficiency in the use of Bioconductor packages.
- Proficiency with use of web repositories such as NCBI, KEGG, ENCODE, UCSC genome browser, etc.
- Experience in web site maintenance, development and cloud deployment of websites.
- Familiarity with high performance computing and computing clusters and experience with version control tools (preferably git).
- Ability and willingness to mentor junior students.
- Ability to provide advice to lab members on appropriate data analysis approaches, and to help implement them.
- Ability to work both independently and collaboratively, and to handle several concurrent projects.
- Exceptionally strong communication and interpersonal skills.
- Excellent data presentation and visualization skills.
- Ability to effectively present complex results in a clear and concise manner that is accessible to a diverse audience.