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Experience in Natural language processing (NLP), Prompt engineering, Embedding, Vector DB. They will use relevant statistical techniques, machine learning models and artificial intelligence algorithms to analyze data, design data models and derive insights to influence actions that maximize business value and effectiveness for the commercial technology segments.
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Apply various Machine Learning technologies (Deep Learning - Natural Language Processing, Autoencoder; Traditional Machine Learning - Isolation Forest, Decision Tree, Random Forest, XGBoost, & LightGBM, advanced analytics (Python, SQL, and AWS) and visualization techniques to extract and analyze various datasets across the company.
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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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Experience with one or more of the following: Machine Learning, Deep Learning, NLP, ranking systems, recommendation systems, backend, large-scale systems, data science, full-stack.
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Earned graduate degree (Ph. D. or M.Sc. + 2 years relevant industry experience) in a highly quantitative discipline (e.g., machine learning, statistics, computer science, applied mathematics, theoretical physics, physical chemistry, econometrics, bioinformatics.
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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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At Skit, I worked mostly on generating informed content on the topics of NLP (Natural Language Processing), AI (Artificial Intelligence) and importance of linguistics and language in building more robust Voice AI systems.
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Hands-on experience with analytics and big data technologies within Microsoft Azure, with experiences in tools such as Azure Data Factory, Azure Machine Learning, Azure Cognitive Services, Azure Databricks and Azure Synapse Analytics.
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You will design and develop and train complex machine learning models. Familiar with all components in the training of ML model, including data loader, backbone search, choice of regressors/objectives/optimizers, hyperparameter search, model finetune, model quantization/optimization/format conversion.
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Artificial Intelligence, Machine Learning, Cybersecurity, Data Security, Security Analytics, Network Security, Identity and Access Management, IT Risk Management, Behavioral Analytics.
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Demonstrated experience with data mining, machine learning, data science and data interpretation techniques. Research, Teaching, and Learning (RTL) at UC Berkeley is looking for an experienced data engineer with a data science background to be part of a new initiative supporting Learning Analytics.
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We are looking for a Machine Learning Software Engineer II to join the Delivery Accuracy team within the Fulfillment initiative. You and your team will own the full cycle of fulfillment-related Machine Learning infrastructure and model development: devising, training, testing, releasing, observing, and optimizing.
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Experience with Artificial Intelligence/Machine Learning (AI/ML) applications for image processing. Develop image processing algorithms for Electro-Optical/Infrared (EO/IR) or Hyperspectral Imagery (HSI) data.
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All efforts driven by the Middle School Literacy Specialist will be in service of an equitable, inclusive, rigorous, and diverse learning experience in reading, writing, listening, and speaking for all students.
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As a Senior Machine Learning Engineer II, you will be responsible for developing ML solutions to increase the safety, efficiency and sustainability of the physical operations. 6+ years experience as an Machine Learning Engineer or similar role.
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