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Data science, machine learning, optimization models, PhD 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, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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We Seek:Deep Learning Ninja: Proven experience in designing and implementing machine learning solutions utilizing GCP and various algorithms (e.g., TensorFlow, PyTorch). Machine Learning Engineer - Google Cloud Platform (GCP)Shape the future with next-generation AI: Join us as a Machine Learning Engineer.
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Data Scientists contribute to projects involving one or more of the following disciplines: Data Science, Applied or Theoretical Machine Learning, Deep Learning, Deep Reinforcement Learning, Physics Inspired ML, Classification Systems, Natural Language Processing, Artificial Intelligence, or Computer Vision.
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MBRDNA is headquartered in Silicon Valley, California, with key areas of Autonomous Driving, Advanced Interaction Design, Digital User Experience, Machine Learning, Customer Research, and Open Innovation.
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A minimum of 5 years of hands-on experience in data science, machine learning, and natural language processing (NLP) roles, including designing, developing, and deploying data-driven solutions.
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The Additive Manufacturing Quality Engineer will act as the command center for all data and trends related to 3D printing development and production performance. 1)Serve as the central hub for performance data and trends, leveraging advanced data analysis techniques to monitor, predict, and enhance 3D printing production efficiency.
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Manage services from Public Cloud Hyperscalers like Microsoft Azure, Amazon AWS, Oracle OCI, Google GCP, and Alibaba Cloud on services like Cloud Compute, Storage Services, Data Services, DevOps, CI/CD, MicroServices, Service Mesh, Containerization, Artificial Intelligence, Machine Learning platforms and End-to-End Observability.
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Azure, AWS, Oracle OCI, GCP, and Alibaba Cloud on services like Cloud Compute, Storage Services, Data Services, DevOps, CI/CD, MicroServices, Service Mesh, Containerization, Artificial Intelligence, Machine Learning platforms and End-to-End Observability.
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Strong technical experience of public/private cloud platforms and services (Azure, GCP, AWS, Alibaba or Private Cloud Tech). Cloud Management training/certification like AZ-30X (Microsoft Azure Architect.
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The ability to understand and implement publications in the fields of Data Science/Machine Learning as applied to marketing. Familiarity with Machine Learning techniques (e.g. classification, clustering, regularization, optimization, dimension reduction, feature engineering.
$126,000 - $140,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Knowledge of Machine Learning, Artificial Intelligence, or Natural Language Processing. Knowledge of text mining or machine learning techniques. Experience developing machine learning models using DataRobot's automated platform.
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The University of California, Los Angeles (UCLA) is seeking an innovative and accomplished leader to serve as the first Chief Data & Artificial Intelligence (AI) Officer (CDAIO) reporting to the Associate Vice Chancellor and Chief Information Officer (CIO.
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Utilize statistical and machine learning techniques to derive insights from large datasets. Implement machine learning techniques to analyze customer behavior and tailor recommendations to individual preferences.
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Assist IT leads to develop IT strategies and to oversee architectural designs for implementation of Advanced Analytics, Big Data and Machine Learning. Work with stakeholders throughout the organization to architect solutions for leveraging advanced analytics, big data and machine learning.
$44.42 - $71.05 an hourFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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U.S. Data Security (“USDS”) is a standalone department of TikTok in the U.S. This new security-first division was created to bring heightened focus and governance to our data protection policies and content assurance protocols to keep U.S. users safe.
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