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    Opportunity: Data Scientist -- Metabolic Modeller @ Successful young Biotech -- Cambridge, UK
    Submitted by Eugene Mc Daid; posted on Thursday, April 12, 2018



    Paramount are working closely with a successful young Biotech based in the heart of Cambridge who are looking for enthusiastic Data Scientists with expertise in metabolic modelling.

    Using expert machine learning and advanced analytics, this progressive start-up is dedicated to identifying new therapeutics for the millions of children that are suffering with rare diseases, for whom there are currently extremely limited treatment options available.

    This Data Scientist position will join an already established and talented team of Computer Scientists, and contribute to the development of in silico drug discovery solutions, using pioneering metabolic modelling approaches.

    This is vital role that will contribute to critical scientific research that could the vastly improve the quality of life for millions of people. This role has the chance to interact personally with the patient groups who will be directly benefiting from this cutting edge technology, making this opportunity hugely rewarding for those who are passionate about the work.


    The ideal candidate will have:
    • A PhD in Computer Science, Mathematics or a related discipline
    • Expertise in the field of metabolic modelling, with experience of COBRA toolbox and Flux Balance Analysis based techniques and results interpretation
    • Proven experience of developing and implementing novel algorithms from scratch
    • Experience of Linear programming/algebra
    • Advanced user of mathematical/statistical programming languages, eg. Matlab, Python, R
    • Graph theory and network analysis
    • Biochemical pathway analysis


    On offer is a competitive salary, flexible working, fantastic location and a wonderfully talented team of people.


    To apply, send your CV to efrancis[at], or for more information, call Emilie on 0121 616 3477.

    Key words: Rare Disease, Orphan Disease, Metabolomics, Metabolic Modelling, Mathematical Modelling, Data Science, Machine Learning, Artificial Intelligence, Cambridge, Personalised Medicine, Drug Discovery, Data Scientist

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