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Master's degree in Business Analytics, Data Science, Finance, Statistics, Applied Mathematics, Economics, or any related field of study, plus at least three (3) years of experience as a Data Scientist or in any related position(s) involving data-oriented programming, data mining/modeling, machine learning, or related quantitative data analysis.
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Machine learning libraries: TensorFlow, PyTorch, Scikit-learn , Deploying and optimizing different pipelines that support various data science processes. We are looking for candidate who can work without any sponsorship #W2 Machine Learning Engineer with 7+ years of experience in designing, building, and maintaining machine learning models and pipelines.
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With tech innovation an integral part of our core business, the booming city proved an ideal backdrop for our heavy investment into machine learning, research infrastructure and big data computing.
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KEY QUALIFICATIONS: Python, SQL Data processing, ML experience Data Warehouse knowledge ETL pipeline knowledge Data Engineering Data Science Data Analytics, Data Mining PLEASE HAVE KNOWLEDGE OF ONE OR MORE OF THE FOLLOWING TECHNICAL SKILLS: Kubernetes, Tableau, PowerBi MongoDB Microsoft Cloud Services like Azure and AKS Snowflake SQL Server data platform Software development (including design, implementation, testing, and change management.
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CrowdStrike is looking for a Sr. Machine Learning Engineer to join our growing ML Platform Engineering Team. Our new colleague will be responsible for building globally distributed, fault-tolerant, and highly scalable ecosystems using Large Language Models.
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Translates business problems into applied statistical, machine learning, simulation, and optimization solutions to advise actionable business insights and drive business value through automation, revenue generation, and expense and risk reduction.
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Knowledge or experience with machine learning algorithms, predictive modeling, and data mining. Drives the advancement of machine learning and artificial intelligence development and application within Operations and Aftermarket Quality.
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Job Description:About Applied AI and Machine Learning at CroweEverything we do is about making the future of human work more purposeful. As a Machine Learning Engineer - Technical Lead II for Crowe Intelligent Document Analyzer (CIDA), you will design the future of this document extraction product as it transitions from a combination of Natural Language Processing and embeddings models to fully integrate with Crowe's in-house Generative AI platform and to leverage Large Language Models (LLMs.
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For this role, you will also need a firm understanding of content writing, natural language processing, linguistics, and machine-learning. ● Understanding of AI technology, machine-learning, natural language processing, and a keen interest in exploring their potential applications with AI-generated content.
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As a Machine Learning Engineer at Nelnet, you'll be at the intersection of data science and software engineering. Collaboration: Work closely with Data Engineers to ensure seamless data integration for machine learning models.
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THE PERSON: A computational scientist, physicist, or engineer with experience in multiple scientific computing domains and experience with using Machine Learning techniques in an HPC setting.
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A Master's or PhD degree in biostatistics, data science, biomedical informatics, epidemiology, public health, computer science, applied mathematics, engineering, or a related field.
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Analysis skills, including electrical device characterization, yield analysis, data mining, DOE, and SPC. JMP or Spotfire experience highly preferred. to ET while also understanding ET data effect on Parametric Yield.
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Solid understanding of machine learning principles, including advanced analytics tools, applied mathematics, ML and Deep Learning frameworks and libraries, and ML techniques.
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We are seeking a senior Applied Scientist with expertise in more than one or more of the following areas: machine learning, natural language processing, computational linguistics, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling.
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