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Understand and apply concepts associated with data modeling, machine learning, and artificial intelligence (AI) and their application in client workflows. Become familiar with common data collection, cleaning, analysis, and visualization technologies including, but not limited to, Python, R, SQL, Cypher, Power BI, Tableau, etc.
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Establish and enforce data governance policies and procedures to ensure data integrity and compliance with industry regulations. 10+ years proven experience as a Data Architect or in a similar role, in-depth knowledge of data modeling, database design, and data warehousing concepts, proficiency in SQL, NoSQL, and other database technologies.
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Dynamis is seeking a Data Scientist to support Homeland Security Investigations (HSI), in Northern, VA by bringing together theory and practice to identify and communicate data-driven insights that allow managers, stakeholders, and other executives in an organization to make more informed decisions on concepts of probability theory, statistical modeling, data visualization, predictive analytics, and risk management.
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Overall data design and modeling across all of the organizations data systems, especially for major data platforms (data warehouses, GCP, data lakes, master data management.
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In-depth understanding of MDM concepts, data modeling, data integration, data quality, and data governance principles. Architect and design end-to-end MDM solutions that align with business requirements, data governance policies, and industry best practices.
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Coordinate with US Agencies and FVEY partners to developed shared production process and standardized governance, data, policy, and tradecraft to support second-party sharing. Ensure interoperability of MARS across targeting, intelligence mission data, experimentation, training, and threat modeling.
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Understanding of data modeling, data warehousing, data quality management and data governance principles. Deep knowledge of Master Data Management (MDM) principles, processes, architectures, protocols, patterns, and technologies.
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Understanding of enterprise data management concepts (Data Governance, Data Engineering, Data Science, Data Lake, Data Warehouse, Data Sharing, Data Applications.
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At least 2 years of experience with data governance. When we launched as a startup we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988.
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5+ years of Technical experience in information architecture, data architecture, data modeling, data governance, ETL design, data quality and BI analytics. Knowledge of tools to support Enterprise Data Warehouse Management Information Knowledge of tools for Business Intelligence (EDW MI/BI) Project Managers and Tech Leads.
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Familiarity with data governance, master data management, metadata management, and related processes. He/she should have prior knowledge of data integration, data warehousing, modeling, business intelligence, and data visualization / presentation concepts.
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This position be involved in various projects including research workflow optimization, innovative tracking, predictive analytics, clinical and operations decision support, advanced analytics, outcomes measures, cost reduction, quality improvement, population health management, statistical and advanced modeling techniques to different types of health, cost, and media data, and coordinate with clinicians and developing recommendations and presentations for a variety of audiences and stakeholders.
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Provide best-in-practice ideas for financial planning, data governance, modeling and related processes. Please refer to our Candidate Privacy Notice for more information about how we process your personal information, and your data protection rights.
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Experience with Master Data Management, Data Governance and Data Quality. Understanding of data modeling and relational databases, expertise in SQL queries and performance optimization.
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Support the enterprise Data Governance framework by implementing and maintaining security controls for data pipelines, enforcing data management policies, and ensuring adequate data quality.
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