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Good programming skills and knowledge of data science tools and libraries in Python or R and experience with Deep Learning frameworks such as TensorFlow. As an ML Engineering Intern, you will have the opportunity to work on cutting-edge machine learning projects, collaborate with experienced professionals, and gain valuable hands-on experience in the field of artificial intelligence and data science.
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The ideal candidate will have a deep understanding and extensive experience in data science, including machine learning, predictive modeling, and data visualization.
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Oversee the continued development of machine learning including supervised machine learning, unsupervised machine learning, deep learning, optimization solutions, image/visual analytics, and natural language processing.
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Deep knowledge of machine learning, predictive analytics, and statistical modeling techniques, with proficiency in data science tools and programming languages such as Python, R, or Scala.
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Product Data Science (PDS) is the data science team working with the R&D on our core Demand Side Platform (DSP) product, and this role will be within the Ads Ecosystem & Measurement (DS) team.
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Proficiency with programming languages including Python, R, SAS, and/or Java. BS in Computer Science, Data Analytics, Data Science, Information Systems, Information Technology, Mathematics, Electrical Engineering, Computer Engineering, or similar technology-related degree.
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Fluency in data science technology stack including R/Python, SQL, Postgres, AWS, and related technologies. Manage data science and business intelligence products hosted by DataKind, primarily in the education and lifelong learning domain.
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Hands-on experience of machine learning methodologies including advanced analytics tools (such as R and Python) along with applied mathematics, Client and Deep Learning frameworks and libraries (TensorFlow, PyTorch, Keras) and Client techniques.
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Requires programming Languages: Proficient in Python, R, SQL, and scripting, experience with Deep Learning platforms (e.g., PyTorch, TensorFlow, Jupyter Notebook)d, and Version control using Git.
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Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
$117,000 - $234,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with Machine Learning, Statistics and Probability, NLP, Deep Learning especially experience in recommendation systems, conversational systems, information retrieval, computer vision, regression modeling.
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The ideal candidate will have a strong background in Data Science, Deep Learning, and Neural Networks. 3+ years of experience in Data Science, Deep Learning, and Neural Networks.
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A deep technical background in applied data science, spanning some or all statistics, experimentation, machine learning, causal methods, optimization techniques, data engineering and architecture (applied to large data), and behavioral analytics.
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You'll code with machine learning tools like Python, R, and Scala. Prior experience in Data Science, Machine Learning, NLP, or Knowledge Engineering. You are passionate about learning and applying new techniques within data science that leads to innovations.
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Large scale machine learning & deep learning models. deep learning, reinforcement learning, and computer vision techniques. Focus areas include customers, stores and employees, in-store service, merchant tools, merchant data science, and search and personalization.
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