Systems Analyst/Programmer II

Organization
Fred Hutchinson Cancer Research Center & Seattle Cancer Care Alliance
Job Location
Job Description

The successful applicant will work with wet-bench scientists to provide biology-based custom data analysis of NGS data (RNA-seq, ChIP-seq and DNA-seq). The position will involve strong programming skills (e.g. R, bash, Python, etc) and significant experience analyzing, integrating and visualizing large genomics data using computational tools such as the R/Bioconductor framework, and online databases and web servers such as the Gene Expression Omnibus and the UCSC genome browser. Essential computational skills to perform quality control of short sequence reads, mapping sequence tags to reference genomes, converting raw-sequence reads and derived data into browser-viewable format. Advanced knowledge and ability to work with R/Bioconductor packages focused on Differential Expression, Peak finding/calling, Motif Identification and Gene Ontology.

The statistical programmer will independently determine programmatic approaches to problems and develop algorithms and software programs that are reusable, computationally efficient and effective. The successful applicant will be expected to not only implement state-of-the art computational algorithms but also to independently develop novel algorithms rlevant to the analysis of high throughput assays.

May include some or all of the following:
1. Analysis
a. Create analysis datasets from statisticians' specifications.
b. Communicate with statisticians (faculty, statistical research associates), laboratory scientists, clinicians, programmers and information systems staff to understand and develop requirements and specifications for analysis datasets and analysis programs.
c. Develop novel computational algorithms that outperform state-of-the art algorithms
d. Create, document and validate programs, data, data pipelines, data distribution plans, and data storage.

2. Data management and high performance computing
a. Implement creative solutions for large volume data management challenges
b. Create high performance computing solutions for processing large data sets

- MS or PhD in Statistics, Biostatistics, or equivalent to work with wet-bench scientists to provide biology-based custom data analysis of NGS data (RNA-seq, ChIP-seq and DNA-seq).
- Suitable applicants should have strong programming skills (e.g. R, bash, Python, etc)
- Significant experience analyzing, integrating and visualizing large genomics data using computational tools such as the R/Bioconductor framework, and online databases and web servers such as the Gene Expression Omnibus and the UCSC genome browser.
- Essential computational skills include performing quality control of short sequence reads, mapping sequence tags to reference genomes, converting raw-sequence reads and derived data into browser-viewable format.
- Advanced knowledge of R/Bioconductor packages focused on Differential Expression, Peak finding/calling, Motif Identification and Gene Ontology are highly recommended.
- In addition, other key qualifications include the ability to work on several projects under the direction of different people, strong oral and written communication skills, and meticulous organization of numerous data files.

We are a VEVRAA Federal Contractor.

If interested, please apply online at http://track.tmpservice.com/ApplyClick.aspx?id=2171490-2647-9121

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