- Bachelor's Degree
- Master's Degree
- Distributed Systems
- Computer Science
The Genomics Research Center (GRC) is a center of excellence for genetics and genomics that supports both Discovery and Development. The GRC plays an integral role towards our goal of developing world class genetics and genomics research, focusing on finding the right targets and helping us better understand not only human disease biology but also the behavior of and response to our drugs in clinical trials. Within the GRC, the Department of Bioinformatics is responsible for data analysis and provides analytical insight for both internal and external data. This involves the identification and characterization of underlying genetic, epigenetic, or genomic factors that are associated with disease diagnosis, prognosis and response (efficacy and safety) to drug treatment, identification of new targets, and interpretation of the impact of genetic and genomic evidence from population-based studies. We have an exciting opportunity for an Engineer – Genomics Data, based in Lake County, IL.
Key Responsibilities Include:
Optimize existing genomic analysis pipelines, workflows, and systems, as well as engineer new pipelines, workflows, and systems based on published or internally developed analytical methodologies
Translate and implement algorithms and protocols in a high performance computing environment
Use and leverage containers for deploying standard pipelines (Docker, AWS Container, etc.)
Develop and maintain data repositories in a structured manner for semi-automated computational reassessment and record keeping
Develop and manage new and existing visualization tools e.g. RShiny, Spotfire dashboard, Java web application, etc.
Position will be hired based on level of experience.
Bachelors or Masters in in Computer Science, Bioinformatics, Software Engineering, Computer and Electrical Engineering, or related field, with typically 5-7+ (BS) or 0-2+ years of relevant experience
Fluency in 2 or more or relevant programming languages, such as Python, Perl, R, nJava, or C++; familiarity with Web Application frameworks such as Flask, Django, nodejs.
Demonstrated proficiency in a Linux environment and high performance distributed computing.
Familiarity with standard tools and data formats related to gene expression, enrichment analysis, genetic, genomic, or epigenetic data, e.g., encountered when analyzing high-throughput transcriptomic, whole exome, whole genome, whole methylome, GWAS, or targeted resequencing data.
Strong communication skills in a collaborative environment
Experience interpreting biological data related to diseases associated with cancer, neurodegenerative disease, immunological, or metabolic disorders are also desired
Experience analyzing and interpreting gene expression data for understanding disease mechanisms and model organism prioritization
Fluency with consortium disease specific (like the Accelerating Medicines Partnership - Alzheimer's Disease, the Cancer Genome Atlas) or genomic databases (such as those relating to genome annotation, genetic variants, public data repositories)