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Basic to substantial experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark, KNIME, RapidMiner, Alteryx, Dataiku, H2O, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine Learning, Microsoft AzureML, IBM Watson Studio or SPSS Modeler, Amazon SageMaker, Google Cloud ML, SAP Predictive Analytics.
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Working knowledge and understanding of the application of data science (including machine learning, natural language processing, neural networks) Our employee resource groups, charity match and volunteer programs keep you connected to your internal Gartner community and causes that matter to you.
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5+ years hands-on experience building search engines, recommendation systems, or other machine learning/artificial intelligence applications in production settings. · Strong working knowledge of Lean product principles, software development lifecycle, and machine learning life cycle.
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The Recruiting Operations team is at the center of powering Gartner's Talent Acquisition machine, by building scalable processes and practices to benefit our global recruiting function. Gartner is growing at double-digit rates, and to do so, Gartner's Recruiting team must hire the best talent to fuel this growth.
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Stay up-to-date with the latest advancements in data science, machine learning, and related fields, and apply them to improve existing processes and methodologies. From CIOs, to leaders in business and government, we help Gartner clients across enterprises translate insights into transformational actions and achieve their mission-critical priorities.
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The policy of Gartner is to provide equal employment opportunities to all applicants and employees without regard to race, color, creed, religion, sex, sexual orientation, gender identity, marital status, citizenship status, age, national origin, ancestry, disability, veteran status, or any other legally protected status and to affirmatively seek to advance the principles of equal employment opportunity.
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How modern data management environments need to be architected to future proof against new emerging and disruptive technologies, such as Data Fabric, machine-learning enabled automation, knowledge-graphs.
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