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    Opportunity: Research Scientist, Machine Learning & Predictive Science @ Celgene -- Seville, Spain
    Submitted by Ana Sanchez Rodriguez; posted on Thursday, July 05, 2018

    OVERVIEW

    Research Analytics is a global team of computational scientists, operating across six R&D sites. We pursue innovative computational research towards Celgene objectives, across domains ranging from bioinformatics, computational biology to machine learning, systems biology and mathematical modeling.

    Current areas of investigation include application and development of novel computational approaches to unravel the mechanism of action of Celgene's pipeline compounds and identify therapeutic targets, methods for integrative analysis of omics data, predictive approaches to patient stratification, and mathematical models of intra- and inter-cellular processes. In addition, we are involved in a range of academic collaborations aimed at understanding disease at molecular and systems level.

    To join our team at the Celgene Institute for Translational Research Europe (CITRE) in Seville, Spain, we are looking for a Research Scientist, Machine Learning & Predictive Science. The successful candidate will work closely with Celgene's global Research Analytics team to develop and apply robust predictive methodologies across drug development programs. Patterns identified from high-throughput molecular data will contribute to discovery research, clinical programs, and prediction of patient response across a variety of oncology and inflammatory diseases of unmet medical need. Also, we encourage our scientists to identify and explore innovative ways for computational research to impact projects and guide Celgene decision making.

    Prior biological or clinical expertise is not required – experience of applying machine learning to real-world problems and a strong interest in the interdisciplinary application of predictive methods to life sciences data are imperative.

    RESPONSIBILITIES

    Responsibilities include, but are not limited to:
    • Implementing and developing contemporary predictive algorithms, evaluating and assessing the most suitable feature space representation and algorithm type for a given problem
    • Translating biological hypotheses into robust and reproducible classification algorithms, suitable for clinical diagnostics
    • Apply advanced predictive models to biomedical data and questions (in-house/public high-throughput profiling, clinical data, integration of multiple data types, mechanistic inference)
    • Contribute machine learning expertise to in-house projects and external collaborations
    • Presentation and reporting of methods, results and conclusions to a publishable standard

    REQUIREMENTS

    • PhD in applied machine learning, computer science, computational biology / bioinformatics or related fields, from a recognized higher-education establishment
    • Prior experience (3+ years minimum) of inter-disciplinary applied machine learning in an academic or scientific research scenario
    • Expertise in algorithmic implementation using both existing software libraries and de novo code and scripting (e.g. R/Matlab, Python)
    • Experience with version control and high-performance computing, including cloud computing and/or big data analytics platforms
    • Collaborative spirit, proven ability to contribute beyond personal projects
    • Ability to understand and communicate the output of computational research to multi-disciplinary scientific teams
    • Verbal and written English language fluency. Proficiency in Spanish would be advantageous
    The position presents a unique opportunity to experience research in an industry setting and to contribute to helping patients with unmet medical need, while maintaining a link to academic research (publication is encouraged).

    TERMS & LOCALE

    The position advertised is fixed-term (24 months) and based in Seville, Spain, reporting to the Sr. Principal Scientist, Research Analytics at CITRE.

    HOW TO APPLY

    Those interested in the position described should submit full CV and cover letter addressed to Virginia Garcia (vgarcia[at]celgene.com).

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