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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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The position of Data Scientist will attend to all stages of data science projects utilizing technologies and tools such as Deep Learning, Traditional Machine Learning, and advanced analytical tools.
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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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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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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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Causal modeling, NLP, time series, deep learning, etc. Develop and Enhance ML Models: You’ll design, build and deploy sophisticated machine learning models that improve our credit decisioning, fraud detection and other processes.
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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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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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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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Incorporate machine learning, statistical modeling, visualization and analytics tools and data sources into the development of processes and techniques to use data effectively and efficiently.
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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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Net, C#, Qlik, Power BI, Machine Learning, Azure Data Factory, RedShift, UiPath, Cloud, RPA, AWS, Redshift, Kinesis, QuickSight, SageMaker, S3, Databricks, AWS Lake Formation, Snowflake, Python, Qlik, Athena, Data Pipeline, Glue, Star Schema, Data Modeling, Performance Tuning, SQL Individual salaries that are offered to a candidate are determined after consideration of numerous factors including but not limited to the candidate’s qualifications, experience, skills, and geography.
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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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