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The Machine Learning Experience (MLX) team supports an internally hosted Model Training ecosystem running critical infrastructure for machine learning and data analysis across Capital One. You will get an opportunity to collaborate on the 1000's of models being trained on the platform by 4000+ users and dive deep into the unique engineering challenges we have around compute, data access, security and scale.
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Whether you join our Statistics, Optimization, or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Marketing, Supply Chain Optimization, Search and Personalization rely on.
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PhD in Computer Science, Operations Research, Statistics, or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis.
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This is an exciting opportunity to make an impact by leveraging AI and ML techniques to create production-level systems through the application of machine learning models. Work in Hadoop, Redshift, and Spark.
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3+ years of experience with distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community.
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Work with a team of developers with deep experience in machine learning, distributed microservices, and full stack systems. 3+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL.
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This team is responsible for Global Customer Personalization products which including batch/real-time analytical, machine learning and modeling solutions using technologies such as Hadoop, Spark, HDFS, MapReduce, Hive, HBase, Python & Java. This young team has delivered industry leading products with many firsts in the organization.
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Familiarity with Hadoop / Spark information architecture, Data Modeling, Machine Learning (ML) 4+ years with Big Data Hadoop cluster (HDFS, Yarn, Hive, MapReduce frameworks), Spark.
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Utilize programming languages like Java, Python, SQL, Node, Go, and Scala, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Center 2 (19050), United States of America, McLean, VirginiaSenior Software Engineer, Back End (Python, Kubernetes, AWS)Do you love building and pioneering in the technology space.
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1+ years of experience with AWS, GCP, Azure, or another cloud service. 5+ years of experience in at least one of the following: Java, Scala, Python, Go, or Node.js. 2+ years of experience with Kubernetes or Docker.
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Center 2 (19050), United States of America, McLean, VirginiaSenior Software Engineer, Back End (Python, Kubernetes, AWS) New York City (Hybrid On-Site): $165,100 - $188,500 for Senior Software EngineerSan Francisco, California (Hybrid On-Site): $174,900 - $199,700 for Senior Software Engineer.
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At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors, who love to solve real problems and meet real customer needs. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC.
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The Opportunity As a Computer Scientist/Engineer on the Fraud Team, you will be at the forefront of developing and executing an innovative strategy utilizing Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), and Multi-Agent Systems to combat a variety of fraudulent activities.
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Machine Learning, Deep Learning, and AI Model Development in Python or R leveraging packages such as SciKit-learn, Caret for example Frameworks such as Tensorflow, Keras, Pytorch, MxNet, and OpenAi.
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