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AWS Graviton powered instances provide the best price performance and support a wide range of computing workloads including application servers, microservices, high-performance computing (HPC), CPU-based machine learning (ML) inference, video encoding, electronic design automation, gaming, open-source databases, and in-memory caches.
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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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Proficient understanding of relational (e.g. Oracle, SQL Server, PostgreSQL) and Big Data distributed structures (Hadoop/Spark) in order to source data effectively. Experience with applied machine learning (tree-based methods, ensemble methods, neural networks/deep learning.
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Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation. At least 1 year of experience with machine learning.
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ALQIMI has developed AOSEN TM- an AI-enabled big data platform, complete end-to-end, systems-of-systems capable of open-source data collections, all-source data blending, analytics, artificial intelligence, visualizations, and more.
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Contribute to support and enable automated data analytics, machine learning and artificial intelligence projects. Proven track record of leveraging open-source capabilities such as Apache Spark, Apache Kafka or other streaming/messaging platforms.
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The Card Loss Forecasting and Allowance team uses machine learning models to forecast and optimize future losses associated with Capital Ones credit card portfolio. Gain exposure to a variety of Build machine learning models/methods through all phases of development, from design through training, evaluation, validation, and implementation.
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You can use Spot Instances for various stateless, fault-tolerant, or flexible applications such as big data, containerized workloads, CI/CD, web servers, high-performance computing (HPC), and test & development workloads.
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At least 3 years’ experience with machine learning. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
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Experience with additional programming languages or big data tools Contributions to data science research or open-source projects VES provides our government and commercial clients with a range of professional and innovation services including: technology innovation acceleration, business consulting services, professional engineering and program oversight, and program and budget support.
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Perform statistical analysis, develop methodologies, and analytical approaches to drive Machine Learning. Source, analyze, transform, cleanse, aggregate, and enrich high volume, velocity, and varied data sets to drive business insights.
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We integrate off the shelf and new development efforts to sustain and enhance the NMEC architecture and by leveraging cloud-based computing, artificial intelligence (Al), machine learning (ML) and cross-domain transfer systems to provide cutting edge data exploitation, enrichment, triage, and analytics capabilities to Defense and Intelligence Community members.
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Source, analyse, transform, cleanse, aggregate and enrich high volume, velocity and varied data sets to drive business insights. Implement predictive models and prescriptive solutions by analysing Big Data sources to improve business performance and mitigate risk.
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Experience leveraging open-source tools, predictive analytics, machine learning, Advanced Statistics, and other data techniques to perform analyses. - Experience in building and deploying production-level data-driven applications and data processing workflows/pipelines and/or implementing machine learning systems at scale in Java, Scala, or Python and deliver analytics involving all phases like data ingestion, feature engineering, modeling, tuning, evaluating, monitoring, and presenting.
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Strong skills in software prototyping and engineering with expertise in applicable programming and analytics languages (Python, R, Spark/Scala) and various open source machine learning and analytics packages to generate deliverable modules and prototype demonstrations of their work.
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