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The Customer Master Data Management Specialist will be part of the Marketing team at Willscot and will own and maintain customer and prospect databases in Salesforce. Identify opportunities for process improvement and optimization related to customer master data management.
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Then come and join our global team as Data Management & Governance Intern to build innovative data solutions supporting our commercial excellence function by leveraging latest technological trends such as Artificial Intelligence.
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The Director, Data Management provides leadership, oversight and implementation support of an enterprise data management strategy to enable the utilization of data as a business asset.
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This position is responsible for Configuration Data Management process for a complex military system or vessel. 2-4 years years of experience with configuration management of provisioning.
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Osi Vision is seeking a Senior Data Management Lead to oversee and supervise a team of contractors supporting Data Sharing and Governance, Data Quality, and Data Standards for the U.S. Citizenship and Immigration Services (USCIS) located in the National Capital Region (Washington D.C)/Maryland.
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CACI is looking for an Asset Management Data Manager, to support our USAF customer in providing a common set of required Enterprise Information Technology services across 187 locations to implement a consistent, high-quality experience for users across all mission environments.
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5+ years' Experience in analyzing and documenting Master Data Management processes, implementing MDM services and capabilities, Data Modeling, Master Data Concepts & Frameworks, Process facilitation, Metadata Management, Data Governance, and Data Quality solutions.
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Support Daiichi Sankyo internal business departments, data steward, and sourcing partners with the following activities: capacity planning, problem management, impact of application issues and overall application (s) stability and associated delays (incident management), operational support for data exchange, integration and data loads, communication on issues and resolutions to DSI.
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Utilizing extensive knowledge of resident data in university enterprise systems, such as PeopleSoft Financials, PeopleSoft HCM, PeopleSoft SIS, FM Systems, Asset Essentials (CMMS - computerized maintenance management system), and other project management software, to recommend analytics tools and dashboards to visualize and report on facility and space data.
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Technical Experience 12 to 15 years of data management, data warehousing, master data management, data governance, reporting and analytics experience, withAt least 8 years of experience in delivering turnkey cloud based data and analytics solutions on AWS (preferred) or Azure ecosystemStrong technical background across AWS stack (S3, Glacier, Athena etc.
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Commodity Analytics helps commodity producers, processors, buyers, and traders across agriculture/softs, metals, energy, and consumer sectors improve commodity price risk capabilities with cutting-edge data science.
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In this role, your work on the team will primarily be in applying advanced analytics to enable better commodity risk management decisions. The Commodity Trading Service Line at McKinsey supports clients in commodity trading and risk strategy, trading operations transformation, and trading and risk digitization driven by deep trading experts with hands-on trading experience and advanced analytics assets.
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Undergraduate degree is required; advanced degree in a quantitative discipline such as computer science (especially machine learning), applied mathematics, economics, quantitative finance or engineering is preferred or equivalent practitioner experience.
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2+ years of commodity markets experience developing trading or hedging strategies (especially physical/cash markets) or price-discovery analysis in basic materials/metals, agriculture, softs, chemicals, plastics or oil & gas preferred.
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You'll work closely with McKinsey's Commodity Trading Service Line to support clients across sectors and geographies. For example, you might work as the lead in maintaining and expanding existing hedging strategies by re-training existing models through process driven approaches.
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Title: data management
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