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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Proven ability to Formulate data science and machine learning strategies to align with business objectives. Experience with large-scale distributed computing and big data technologies (databricks etc.
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The Machine Learning Engineer will assist with large data pipelines of satellite imagery and help build computer vision models to detect and classify maritime objects in imagery data.
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We are seeking a highly skilled and motivated Senior Applied Machine Learning Engineer for our Federal Delivery team who is passionate about delivering value to US Federal Government clients with Generative AI. If you have experience harnessing data to deliver mission impact at enterprise scale, enjoy working with alongside product teams to iteratively improve capabilities, and thrive in client-facing technical environments, we would love to hear from you.
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Participate in partnerships with data science leaders, like our partnership with NVIDIA to deliver Deep Learning Institute (DLI) training to the federal government. You’ll develop algorithms, write scripts, build predictive analytics, apply ML and deep learning, and use the right combination of tools and frameworks to turn that set of disparate data points into objective answers to help drive innovation, research, and development for our military and government leaders to make informed decisions.
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6+ years of professional development experience as Machine Learning Engineer, Data Scientist or related role 6+ years of programming experience with modern languages such as Python, R, C#, Java, or Scala.
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To join our team in Washington, DC. In this role you will work across our client engagements, providing expertise in machine learning algorithms, natural language processing (NLP), data collection, data analysis, data mapping, data profiling, data mining, data modeling, and data visualization.
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They will use relevant statistical techniques, machine learning models and artificial intelligence algorithms to analyze data, design data models and derive insights to influence actions that maximize business value and effectiveness for the commercial technology segments.
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Qualifying coursework may include—but is not limited to—data science, statistics, machine learning, optimization, numerical analysis, scientific programming, computational methods, supervised learning, unsupervised learning, text mining, and image analysis.
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Contribute to interdisciplinary solutions that combine data engineering and Artificial Intelligence/Machine Learning to integrate predictive models. A successful candidate should be able to demonstrate technical proficiency with these mentioned Machine Learning tools and have a proven track record engineering software solutions to process and analyze unstructured data sets.
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. We are a multidisciplinary team with expertise in machine learning, data engineering, and software development.
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We are hiring a talented Senior Big Data Engineer to build cloud-based data pipelines for machine learning, data processing with Apache Spark, and database development.
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Transform your career to the next level with GDIT as a Solutions Architect where you can elevate your skills in Data Science, Machine Learning, or leading AI techniques and technology expertise into solutions in support of our Government customers.
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Oversee the implementation of data processing workflows, analytics, and machine learning models using Databricks. This role requires a deep understanding of big data technologies, particularly Apache Spark, as well as expertise in data engineering and analytics.
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This high performing team works with clients to implement the full spectrum of data analytics and data science, from data architecture and storage to data engineering and querying, to data visualization and dashboarding, to predictive analytics, machine learning, and artificial intelligence as well as robotic process automation (RPA.
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