Job Description :
Responsibilities include, but are not limited to, the following:
Implement data solutions to disseminate and visualize datasets using contemporary application platforms (Shiny, etc)
Enable knowledge collection across R/ED by creating knowledge bases from the analysis of public and internal data sets and integration with annotation resources
Use skills to enable on-going innovation of data management systems, processes and procedures to enhance R/ED productivity
Make data, including raw/interim data, available to R/ED department personnel as required
Acquire user feedback to inform business requirements for future data systems development.
Help develop, enhance, and automate processes for queuing and prioritizing data management and curation requests

Experience and Education
Bachelor’s degree in a relevant discipline with at least 4 years’ experience, Master’s degree with at least 2 years’ experience or PhD
Demonstrated proficiency with molecular biology assay concepts and ability to support, develop and deploy laboratory and other research data management processes and procedures as they apply to complex, high dimensional data sets
Extensive practical experience working with diverse but highly-connected scientific knowledge collections and their query interfaces to enable research hypotheses around compound targets, mechanisms of action, and patient response
Demonstrated proficiency with current software engineering methodologies, such as Agile, source control, project management and issue tracking
Working knowledge of Rest APIs and container strategies strongly preferred.
Excellent skills in R programming and experience in additional computer languages such as Perl, Python, or Java (or C/C++)
Experience producing visualization of data sets (eg., R/shiny, Spotfire, etc)