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Big Data Technologies: Distributed Computing: Tools like Apache Spark, Hadoop. Big Data Storage: Technologies such as HDFS, Apache Cassandra, or cloud storage solutions. Software Engineering & DevOps: Version Control: Familiarity with tools like Git. Continuous Integration/Continuous Deployment: Knowledge of pipelines and tools like Jenkins, Travis CI. Containerization: Familiarity with Docker, Kubernetes, etc., for scalable deployment of data science applications.
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Experience with big data analytics, machine learning, artificial intelligence, or anomaly detection. Leverage big data analytics and traditional security event types to identify advanced threats or indicators of compromise.
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Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.
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T-Rex offers both IT and professional services to numerous Federal agencies and is a leader in providing high quality and innovative solutions in the areas of Cloud and Infrastructure Services, Cyber Security, and Big Data Engineering.
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Experience in CI/CD pipeline and related open-source tools like GIT/Jenkin/CircleCI/SonarQube and knowledge in Terraform will be a big plus. Experienced in complex system design and data pipeline and architectures, with good knowledge on Docker and Kubernetes.
$76,000 - $185,000Full-timeRemoteExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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Are you looking for an opportunity to combine your technical skills with big-picture thinking to make an impact in Digital Engineering and Unmanned Maritime Systems? You'll develop your skills in modeling, simulation, and data analysis while gaining experience with unmanned and autonomous vehicles.
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Significant experience with General Ledger (GL) technology including but not limited to data integration, accounting rules engines, and financial data hubs/warehouses/marts. Responsible for gathering data, creating reports, documenting and assessing Internal Control over Financial Reporting (ICFR), and producing evidence of control operational and design effectiveness for facilitate the delivery of auditable evidence to internal and external auditors.
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Big data” doesn’t faze you. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. The US Card Data Science team builds industry leading machine learning models to empower core underwriting decisions in the acquisitions and management of new and existing credit card customers.
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Whether its big or small data, our teams ability to turn that data into insights and insights into great decisions, hinges upon the code that makes it all work. PhD in STEM field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics.
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Our capabilities range from C5ISR, AI and Big Data, cyber operations and synthetic training environments to fleet sustainment, environmental remediation and the largest family of unmanned underwater vehicles in every class.
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Manage, architect and analyze big data in order to build data driven insights and high impact data visualizations. The ideal candidate will take their place amongst a team of analysts and engineers and help them leverage the power and features of Tableau to develop highly impactful data visualizations.
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As a Data Scientist Intern at Big Tech Company, you will be responsible for establishing a data-driven culture of exploitation for our consumer products. Data Scientist Intern | Big Tech Company.
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Experience solving Big Data batch and real-time data processing problems, with large-scale analytics and data processing engines such as Apache Spark and Databricks. Knowledge of Python data science tools, such as SciKit, Numpy, TensorFlow, Jupyter.
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May recommend data analytics strategies including enterprise data warehousing, master data management, data quality, data governance, Big Data, business intelligence, visualization.
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