50-80%. The amount of time the average data scientist spends preparing data. You will help drastically reduce this number to unlock efficiencies in how we discover new drugs.
Passionate about making connections between data sets at scale to unearth more needles from many more haystacks? We are looking to fill a position that sits precisely at this point in early computational drug discovery: between large-scale processed raw data on one side and individual molecular insights on the other side. If you are a versatile data scientist who enjoys casting problems into generic computational solutions to catalyze efficiencies in data-driven drug discovery, this is for you.
Your responsibilities include but are not limited to:
* Engage with computational peers across the research organization to identify recurrent problems that can be solved at scale, focusing on all data domains that are of practical use in drug discovery. * Design, implement, and maintain robust methods, algorithms, and packages (python, R) that help the computational community solve old and new problems with ease. * Define, refine and promote the computational glue that is between large-scale data processing (such as NGS pipelines) and insights at very detailed level. * Ideate and implement visualizations, dashboards & webservices for data dissemination to computational peers as well as to non-computational collaborators.
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