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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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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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Possess deep functional and technical understanding of the Machine Learning technologies (Google’s Cloud Platform, custom and COTS-embedded) and provide prescriptive guidance on how these are leveraged across the Fuse landscape.
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You will design and develop and train complex machine learning models. Knowledge on image/video/depth data processing is desirable. Our engineers develop resilient and scalable solutions on internal projects as well as external projects with our partners across animation, visual effects, mixed reality, social media and game production.
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Continuously improve knowledge and abilities in the areas of software and data engineering, statistical learning, and machine learning. Implement new statistical, or machine learning, models and maintain existing models, used in all facets of player acquisition and game play.
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Performs basic trial runs to check accuracy of machine settings or programmed control data. At LISI AEROSPACE, we focus above all on advancement and flexibility. The Production Machine Operator individual performs the basic set up and operates automatic machines in accordance with established shop practices.
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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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4+ years of hands-on experience with OpenShift Container Storage (OCS) and OpenShift Data Foundation (ODF). Seeking an experienced, passionate, hands-on OpenShift Kubernetes Platform Engineer/SRE/Architect to join our dynamic platform engineering team within the Enterprise Software Products and Capabilities organization.
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In Support Product we build highly available and scalable, Machine Learning driven products that integrate with the broader Cash App ecosystem to help customers solve their problems. The team currently consists of server engineers, designers, and product management, machine learning modelers and applied machine learning engineers located in Australia and the United States.
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Strong technical understanding of data modeling (data vault 2.0), data mining, master data management, data integration, data architecture, data virtualization, data warehousing and data quality techniques with hands on experience using data management technologies like Informatica PowerCenter/IICS, Collibra, Master Data Management, DBT Cloud, Denodo and Golden Gate.
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Develop and execute tests to support dynamic systems development and qualification, including hands-on work like test article integration, instrumentation setup, data processing, and model validation.
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You thrive on building data solutions and analytics tools, drawing actionable conclusions and digging deeper to uncover trends that will improve our machine learning systems.
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Understands data to be mined from the service ticketing system, and pulls data to create KPI dashboards on first-time fix rate, response time, field work order status, etc. Become a subject matter expert on the process and tools used within KARL STORZ.
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Fabletics/TechStyleOS is looking for a Data Analyst who will be responsible for the management, analysis and visualization of data and reporting while utilizing industry-leading data management and data visualization solutions (e.g., MicroStrategy, Tableau, Power BI) in order to support the Senior Leadership Team and other key business stakeholders.
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As a part of this client-centric team, which is focused on Cartier employee development throughout the talent lifecycle, you will work cross-functionally with various levels of leadership, as well as a variety of subject matter experts within the organization, to identify learning needs, then implement solutions.
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