Biological Data Scientist (DDM)
Location:
Collegeville , Pennsylvania
Posted:
November 21, 2017
Reference:
WD105889
Basic qualifications:
• MS or equivalent with relevant experience in Computational Science
• Evidence of a broad knowledge of computational sciences which will include a deep knowledge of one or more areas of machine learning and analysis of high content datasets
• Demonstrated ability to build robust statistical models from large scientific data sets
• Demonstrated ability to utilize computer programming and scripting languages such as R, C/C++, Python or Java.
• Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in the Life Sciences
• Evidence of strong critical thinking skills
• Excellent written and oral communication skills and the ability to interact effectively with scientists in other disciplines, in particular bioassay scientists, with a positive, collegial, collaborative attitude
• A keen interest or some background in Biology

Preferred qualifications:
• PhD in a STEM discipline
• Demonstrated skills in building models based on high content biological data
• Demonstrated skills to programmatically collect, combine, mine and analyze complex biological and chemical data
• Experience with scientific pipelining tools such as Pipeline Pilot or Knime.

Details:
Job Purpose:
Drug Design and Selection (DDS) supports the therapeutic areas within GSK contributing to the discovery of new medicines through the collaborative identification of disease-linked targets and the selection of molecules for future drug development. We are responsible for delivering scientific expertise and specific platform capabilities to ensure that GSK is able to deliver the best modality and molecules, for development, in a particular disease area. Within DDS, there are a number of lines which support this process, working with the therapeutic areas to design and deliver the high quality molecules which are necessary for successful clinical evaluation.

We are seeking to introduce new biological screening technologies to our scientific platforms, with a focus on high content, data rich detection methods such as transcriptomics, imaging and flow cytometry. This combination of high content and high throughput will require the development and application of modern machine learning algorithms g if we are to get full value from the data. We therefore have a vacancy for a Biological Data Scientist with deep expertise in modern machine learning techniques, experience of handling large high content biological datasets, and an interest in the application of these methods to biological data.

The Computational and Modeling Sciences Department (CMS) generates and applies computational models across the drug discovery pipeline from lead discovery to late stage development. We work closely with multidisciplinary drug discovery teams, applying computational approaches combined with knowledge of chemistry, biology, and the drug discovery/development process to facilitate the identification, prioritization and progression of bioactive agents.

Our scientists are responsible for generating testable hypotheses that drive discovery programs, extracting and analyzing decision-making data. The successful candidate will work collaboratively with other team members to implement innovative analytics and machine learning solutions that are capable of handling large multiparametric datasets from high content compound screening approaches. They will be embedded within the biology teams to enable the seamless integration of high content biology with state of the art data analysis. They will also contribute to the training and development of data science capabilities in our laboratory-based scientists.

Key Responsibilities:
• Develop methods for the analysis of multiparametric datasets from high content screening
• Input to the assay design phase to define statistically significant biological endpoints.
• Use machine learning to build predictive models for application in drug discovery programs
• Develop automated capabilities which enable the application of machine learning across the discovery portfolio, from small molecules to biopharmaceuticals.
• Be an expert resource and consultant for other members of Drug Design and Selection
• Prepare and present results to internal and external groups
• Work with others within a matrix organization

Contact information:

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