Stanford's Curtis Laboratory is seeking a bioinformatician/Research Data Analyst 1 to join the Cancer Computational and Systems Biology group to analyze and interpret large sequencing datasets by applying and developing bioinformatics and statistical methods for cancer research, aiming to improve diagnosis, treatment, and earlier detection of cancer.
Requirements
- Strong background in bioinformatics and biostatistics, including analysis of high-throughput sequencing data
- Experience working within a UNIX/Linux environment
- Fluency in programming languages such as Perl, Python, Java, R, C, Matlab; MySQL
- Familiarity with machine learning, information theory and signal processing.
- Experience with algorithm development
- Familiarity with basic molecular biology
- Background in cancer biology
Responsibilities
- Analyze and interpret large sequencing datasets by applying and developing bioinformatics and statistical methods for whole genome/exome sequencing, RNA-seq, ATAC-seq, single cell and spatial datasets.
- Employ new and existing tools to interpret, analyze, and visualize multivariate relationships in data.
- Create databases and reports, develop algorithms and statistical models, and perform statistical analyses appropriate to data and reporting requirements.
- Use system reports and analyses to identify potentially problematic data, make corrections, and determine root cause for data problems from input errors or inadequate field edits, and suggest possible solutions.
- Develop reports, charts, graphs and tables for use by investigators and for publication and presentation.
- Analyze data processes in documentation.
- Collaborate with faculty and research staff on data collection and analysis methods.
Other
- Bachelor's degree (or higher) in Bioinformatics, Computer Science or a related field with one year (min) of relevant experience
- Excellent communication and team skills and fluency in both spoken and written English
- Bachelor's degree or a combination of education and relevant experience.
- Experience in a quantitative discipline such as economics, finance, statistics or engineering.
- Substantial experience with MS Office and analytical programs.
