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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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You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
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Proven track record of leveraging open-source capabilities such as Apache Spark, Apache Kafka or other streaming/messaging platforms. Contribute to support and enable automated data analytics, machine learning and artificial intelligence projects.
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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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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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At least 1 year of experience in open source programming languages for large scale data analysis. You’re comfortable with open-source languages and are passionate about developing further.
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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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The Leidos has an exciting opening for you, as a Cloud Engineer, to play a critical role with design, engineering, development, deployment, and use of software for the DOMEX Technology Platform (DTP) where we support our client's mission to centralize and standardize Tasking, Collection, Processing, Exploitation and Dissemination (TCPED) of Open Source Intelligence (OSINT) across the DoD and IC enterprise.
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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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Team Description The Compliance Risk Management Analytics and Innovation team builds the machine learning models that contribute to the organization’s regulatory compliance and risk management program.
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High proficiency in using Python or Scala, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, NiFi, Scoop), SQL to build Big Data products & platforms. Currently, we are enhancing our customer experience with new user interfaces, moving to API-based data publishing to allow for seamless integration in other Mastercard products and externally, utilizing new data sets and algorithms to further analytic capabilities, and generating scalable big data processes.
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Team Description Business Card & Payments Data Science team builds industry leading machine learning models to empower credit underwriting decisionings, supports advancement in Capital One business card product strategies, decisioning and credit infrastructures.
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You’re comfortable with open-source and are passionate about developing further. You have experience with clustering, classification, sentiment analysis, time series, and deep learning.
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Technical background with machine learning, deep learning, open-source software, Artificial Intelligence/Machine Learning Developer. Data Scientist (Big Data Developer.
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