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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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Far Ridgeline Engagements LLC (a QinetiQ US Company) is seeking an experienced cloud data engineer/developer to help lead a growing team with a US Department of Defense operations support project developing modern, cloud-based solutions to empower customers to securely store and analyze data.
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As a machine learning engineer, you'll train, test, deploy, and maintain models that learn from data. Experience with artificial intelligence, data science, ML engineering, data research, software engineering or data analytics.
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Assess, transform, organize, and optimize data for use by machine learning algorithms. We follow SAFe agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate-first", use modern tech stacks, and constantly challenge each other to grow and improve.
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This candidate should be proficient in using BI tools such as Tableau, in addition to experience with one or more modern technologies such as relational databases, AWS, Snowflake, Python, Databricks, Git, and Machine Learning.
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OSCAL Integration: Work closely with our FedRAMP teams to integrate OSCAL machine readable data formats into our software solutions, to ensure that cloud service offering (CSO) security controls are accurately represented, implemented, and are capable of being assessed in an automated way.
$61,600 - $190,900 a yearExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Knowledge of machine learning, artificial intelligence, or data analytics. Experience leveraging a modern web framework (such as React or Vue) and related technologies. While responding to these challenges, you will leverage and grow your hands-on skills with software design and development, engaging in the full software engineering lifecycle using modern technologies.
$150ExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure Client (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Experience with modern front-end frameworks/libraries such as React, or Vue.js. Our developed systems empower our customers to deliver Live as well as On-Demand Video Streaming to their audience, store and share multimedia content as well as perform Video and Audio Analysis using Machine Learning.
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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Leverage a broad set of modern technologies – including Python, R, and Spark – to analyze and gain insights within large data sets. Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for Compliance-Complaints.
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3+ years of data engineering experience with modern big data analytics architectures (Hadoop, SQL, HIVE, Spark, Snowflake, etc.) Apply data mining, NLP, and machine learning (both supervised and unsupervised) to improve relevance and personalization algorithms.
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As a Data Scientist at Capital One, youll be part of a team thats 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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Subject matter expertise in one or more of the following: Artificial Intelligence (AI), Natural language Processing (NLP), Machine Learning (ML), Statistical Models, and Text Analytics on large data sets.
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You have worked with imagery data, overhead imagery (optical or Synthetic Aperture Radar (SAR. You are familiar with machine and deep learning libraries such as Scikit-learn and PyTorch. As a Data Engineer, you will have the unique opportunity to curate and organize data for a program advancing state-of-the-art modeling and prediction capabilities.
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