Bioinformatics Scientist (51683)

Position Objective: The Bioinformatics Scientist will provide support to the National Institute of Environmental Health Sciences (NIEHS) within the National Institutes of Health (NIH).

The position will independently provide support services to satisfy the overall operational objectives of the National Institute on Environmental Health Sciences.

• Coordinate with biologists and/or other bioinformaticians in the design of models summarizing and explaining experimental data, provide interpretive analyses of data derived from different experimental platforms to generate biological meaning
• Prepare reports and publication quality graphics summarizing experimental data including providing written documents
• Participate in meetings with biologists; present findings to individuals and groups
• Conduct analyses on NextGen sequencing data including data derived in ChIP-Seq, RNA-Seq, miRNA-Seq and other experimental models 1
• Conduct analysis and interpretation on data from genomic platforms including expression, exon, tiling, promoter and others array types 2
• Conduct data analysis and interpretation on other large data types in genomic context 3
• Establish and maintain workflows including experimental design, analyses of data quality, genome and meta-genome integration and others 4
• Write utility scripts, macros and/or custom programs or algorithms in support of biological discovery 5
• Work products and documents related to conducting data analyses on NextGen sequencing data; analyzing and interpreting data from genomic platforms, including expression microarrays and others.
• Work products and documents related to identifying and discovering analyses of differentially expressed genes; calculating fold change, p-values, multiple test adjustments and custom analyses; conducting data quality analyses/assessments, analyses of raw and normalized data using graphical techniques.
• Work products and documents related to developing and/or managing workflows, experimental design, analyses of data quality, genome and meta-genomic context and others.
• Work products and documents related to writing custom programs and algorithms; designing models summarizing/explaining experimental data; preparing reports and publication quality graphics; participating in meetings.