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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Experience with artificial intelligence, large language models, machine learning, and natural language processing techniques. Demonstrated expertise in one or more areas of data management, data science, and/or data engineering.
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Data Engineering skills to include:Experience with preparing data for analysisStrong understanding of data science conceptsExperience with BI tools Understanding ML frameworks, algorithms, and libraries.
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BRMi is seeking a Senior Data Scientist to provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for Compliance-Complaints.
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Advanced knowledge of advanced techniques such as: dimension reduction techniques, natural language processing, sentiment analysis, anomaly detection, geospatial analytics, etc. Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for Compliance-Complaints.
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Contribute research to top-tier NLP conferences such as Association for Computational Linguistics (ACL) and Empirical Methods in Natural Language Processing (EMNLP) You will work with a seasoned group of Natural Language Processing (NLP), Speech, and Computer Vision specialists, experimenting with emerging technologies in generative AI, delivering software implementing these technologies, and contributing research to major NLP and AI conferences.
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2+ years of experience working with modern AI capabilities in machine learning, including deep learning and natural language processing, and LLM frameworks. 5+ years of experience with artificial intelligence, data science, ML engineering, data research, or data analytics.
$125,600 - $285,000 a yearFull-timeExpandUpdated 1 month ago - UpvoteDownvoteShare Job
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Focused experience in artificial intelligence (AI), including machine learning and natural language processing (NLP) Skilled in documentation and database reporting for the purposes of analysis, data discovery, and decision-making with the use of relevant software such as Crystal Reports, Excel, or SSRS.
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Amazon has been investing in Machine Learning for decades, and by joining AWS you’ll join a community of scientists and engineers developing leading edge solutions for enterprise-scale data science applications.
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The Oracle Government Defense & Intelligence (GDI) Cloud Adoption Engineer position, a member of the Cloud Solution Engineering job family, is a highly visible and influential individual contributor role.
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It leverages technologies like robotic process automation (RPA), machine learning, natural language processing, and blockchain to help its clients enhance operational efficiency, improve customer experience, and drive business growth.
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These engagements will focus on Real Time and Batch-based Big Data processing, integration and governance, Business Intelligence, Visualizations, data science and Machine Learning.
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Data Techniques: Advanced knowledge of techniques like dimension reduction, natural language processing, sentiment analysis, anomaly detection, and geospatial analytics. The ideal candidate will provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission-critical decision-making for Compliance-Complaints.
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Capture and share best-practice knowledge amongst the worldwide AWS solution architect community. In this technical role focusing on Data Analytics and ML, you will architect enterprise-grade AWS solutions, own the technical relationships and deliverables for a GSI, develop and execute multi-day workshops, lead white boarding sessions, author white papers and blog posts, and respond to complex technical inquiries including RFPs. You will partner with Sales, Business Development and the Data & Analytics Service teams to enable partner’s Data & Analytics services adoption.
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A certification such as Red Hat Certified Engineer, Puppet Certified Professional, AWS Certified Solution Architect, Certified Kubernetes Administrator (CKA), or Docker Certified Associate (DCA.
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