Research Bioinformatician I

About the Role

The Research Bioinformatician I (RBI) performs general bioinformatics analysis and software support for projects involving omics. The RBI manages omic data, including loading and querying data from database systems and public repositories, and transforms and merges multilevel omic data into user-friendly formats. The RBI assists in the development, testing, and maintenance of modular software pipelines, provides daily bioinformatics data analysis, preliminary interpretation of data, and reports of results for possible publications.

Key Responsibilities

• Provides general bioinformatics analysis support for omic projects.
• Responsible for data analysis and software compilations including next-generation sequence alignment, polymorphism identification, expression analysis, and visualization tools and browsers.
• Summarizes data analysis results in a form suitable for the first draft of written reports and makes preliminary interpretations of the data.
• Assists in the development, testing, and maintenance of modular software pipelines for genome sequencing, assembly, annotation, metagenomic analysis, and genotyping using high-throughput sequencing platforms, including 454, Illumina, and Pacific Biosciences.
• Manages data, including loading and querying data from database systems, downloading omic data from public repositories, and transforming data to the necessary formats.
• Helps to identify, evaluate, and incorporate relevant algorithms and software pipelines by reviewing pertinent literature in bioinformatics and computational biology.

Requirements & Tech Stack

• Bachelor's Degree in Computer Science, Electrical Engineering, Computational Biology, or Bioinformatics, or relevant fields (e.g., Biology with strong quantitative training, Biostatistics with concentration in Bioinformatics) required. Master's Degree preferred.
• No experience required. Three (3) years of experience in a research environment preferred.
• Background and work knowledge in algorithms, scientific computing, and machine learning or statistics.
• Familiar with C/C++, Java, Perl, Python, and the Unix (Linux) environment preferred.
• Experiences in manipulating, analyzing, and annotating very large genomic (e.g., NGS) data sets, both in exploratory and pipelined fashions, will be a plus.
• Experience with nucleic acids methods strongly desired.