Machine Learning Data Scientist Postdoctoral Fellow - GlaxoSmithKline plc


: $75,876.67 - $153,250.00 /year *

Employment Type

: Full-Time


: Information Technology

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Are you an exceptional postdoc or PhD scientist with strategic decision-making skills? If you would like to work on critical R&D challenges in the pre-competitive space then consider joining our postdoctoral R&D program.

Post-doctoral fellows within GSK s global collaborative training program will have the opportunity to:
Be part of cutting edge science to advance GSK s mission to improving the quality of human life by enabling people to do more, feel better, and live longer .
Accelerate your post-doctoral research with GSK s extensive R&D engine.
Bridge the gap from target identification to medicine for patients in-need by leveraging GSK s: Tools, Technologies, Libraries, Samples, People, Knowledge Base, & Training program.

Postdoctoral research will be focused on addressing the critical R&D challenges that have been selected as cutting edge science and are optimal areas of science for post-doctoral training.

Your Postdoctoral experience will be accompanied by tremendous support at GSK with the opportunity for daily collaborations with world-class scientists. GSK is committed to providing each postdoc with a mentor who will be focused on providing a comprehensive training program and are expected to nurture skills that will enable postdocs to become independent scientific investigators.

Postdocs are actively encouraged to publish and present their research at internal and external meetings and participate in the GSK R&D postdoc community, which will include scientific and social programming.

Post-doc Experiences and Responsibilities:
The Discovery Biology Automation (DBA) team is focused on developing an automated compound screening platform that leverages assays that more closely model in vivo systems and recapitulate relevant biology. This work is on the cutting edge of drug discovery and applied artificial intelligence. Research in the DBA team is focused on GSK s current ambition to build a cross-departmental Imaging Center of Excellence that will support groups across the Discovery space. A postdoctoral researcher in DBA will investigate novel deep learning-based methods to analyze readouts from assays of this type, determining the proper architecture for these networks; data requirements for successful classification and analysis; and appropriate controls for biological noise inherent in highly complex biological systems. The increased complexity of the systems DBA will prosecute will require creative strategies for properly training deep architectures for analysis. A postdoc in DBA will have the chance to investigate the transferability of learned features between cell types and assays to establish methods that can be deployed strategically to improve the efficiency of the DBA computational workflow. Finally, a postdoc in DBA will have the opportunity to be part of efforts to generate phenotypic fingerprints of segments of the immense GSK compound collection in disease-relevant systems. Phenotypic data generated on the DBA platform can be combined with other -omics-level data about compounds in the GSK collection to more quickly identify promising candidate molecules. Since these data may be challenging or expensive to obtain, a postdoctoral researcher in our group will have the opportunity to investigate innovative methods to link phenotypic profiles with structural attributes of molecules to provide mappings from phenotypic space to chemical space, further enabling in silico molecular design.
This role will be located in Upper Providence (Collegeville), PA.
Please use the cover letter to highlight how you fit the applicant profile and meet the skills and competencies for the role. Your cover letter along with your CV will be used to assess your application.

Basic Qualifications

Ph.D. and/or a M.D./Ph.D. in computer science, bioinformatics, biochemistry, biology, or related field.

The qualified candidate should have experience using machine learning packages to train, deploy, and interpret various algorithms such as random forests and deep neural networks. The qualified candidate should also be comfortable navigating complex biological and/or chemical data sets.

To be successful in the role, this person must have the ability to:
understand, interpret, and communicate complex scientific data;
must be comfortable conversing with opinion leaders regarding highly technical information;
must be a self-starter able to handle multiple projects concurrently;
possess a good balance of creative, analytical and strong communication skills;
ability to be productive and successful in an intense work environment and
he/she must be a Team Player.

Preferred Qualifications

Proficiency with Python or a similar programming language; experience with TensorFlow, Keras, or similar deep learning packages; experience with phenotypic screening, image analysis, and/or cell painting; prior experience with collaborative programming projects.



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