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Conduct advanced quantitative research, using machine learning (ML) and natural language processing (NLP) techniques to understand patterns in large volumes of data, identify relationships, detect data anomalies and classify data.
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Experience with a wide range of AI techniques, including machine learning, deep learning, natural language processing, and computer vision. Advanced degree (PhD or MS) in Computer Science, AI, Machine Learning, or a related field.
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Utilizes methods particularly in the areas of modeling, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and experience designing/implementing solutions.
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Python or related language- Experience with neural deep learning methods and machine learningPREFERRED QUALIFICATIONS- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
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MUST HAVE Master’s in Computer Science, Computer Engineering or related Engineering, Machine Learning, or related field; 3 yrs relevant experience in one of the following: Machine Learning, Natural Language processing, or Ontology Engineering technologies; and required skills.
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Deep expertise in one or more data science specialties, such as natural language processing, computer vision, or deep learning. + Develop models and solutions for problem sets in a broad range of data science areas, including but not limited to natural language processing, graph analytics, computer vision, anomaly detection, data visualization, and metrics creation.
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Data Science Management, Director Data Science, Director of Data Science, Director of Engineering, Senior Director of Data Science, Senior Director Engineering, Manager Data Science, Manager Engineering, Artificial Intelligence Management.
$280,000 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Data Science, Decision Analytics, Data Mining, Information Sciences, Information Technology, Quantitative Analysis, Operations Research, Operations Analytics, MBAs with Analytical Focus, Engineering, Computer Science, Statistics, Data Management, Decision Science, Quantitative Science, Mathematics, Information Systems, Industrial Engineering, Artificial Intelligence, Computer Vision, Machine Learning, Natural Language Processing.
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Experience in one or more areas of computer science, such as Natural Language Processing/Understanding, Neural Networks, Computer Vision, Machine Learning, Deep Learning, Privacy, Trust & Safety, Software Engineering or similar.
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Select and apply advanced modeling and machine learning techniques, including regression models, tree-based learning, neural networks, clustering techniques, and natural language processing, to deliver insightful and accurate analyses.
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Your Role and Responsibilities As a Senior Data Scientist at IBM, you will work to solve business problems using leading edge and open-source tools such as Python, R, and TensorFlow, combined with IBM tools and our AI application suites.
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Doctorate’s degree in Statistics, Analytics, Mathematics, Physics, Computer & Information Science, Computational Biology, Economics, Experimental Psychology, Neuroscience, Engineering or other quantitative fields of study or relevant experience and/or education as determined by the company in lieu of Doctorate’s degree.
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PhD or MS degree in Engineering, Technology, Computer Science, Machine Learning, Statistics, Mathematics or related quantitative / engineering field. Experience in reinforcement learning and natural language processing.
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Developing machine learning, data mining, statistical network, natural language processing, text analytics, and graph-based algorithms to analyze massive data sets.
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Support research conducted in ISD in projects focused on the design of experiments, time series analyses, anomaly detection, natural language processing, large-scale data infrastructure, test and evaluation, computer vision, cyber security, and systems engineering.
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