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The Anti-Money Laundering Modeling and Advanced Data Insights team is on a journey to modernize the way Capital One identifies potential money laundering, terrorist financing, and human trafficking through the use of machine learning, statistics, and other advanced analytic techniques.
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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. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
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This high performing team works with clients to implement the full spectrum of data analytics and data science, from data querying and data wrangling, to data visualization and dashboarding, to predictive analytics, machine learning, and artificial intelligence as well as robotic process automation (RPA.
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Apply machine learning, econometrics/statistics, predictive modeling, return-on-investment analysis, simulation, and data visualization methods to support program evaluations and the development of health policy.
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Roles and Responsibilities: Conducting cutting-edge research in the fields of artificial intelligence, machine learning, and robotics, specifically as they relate to autonomous vehicle technology.
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Together, Appen and Figure Eight Federal, combine the best of human and machine intelligence to provide high-quality annotated training data that powers the world’s most innovative machine learning (ML) mission solutions.
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Candidates from all areas of geotechnical engineering with crosscutting expertise in one or more of the following areas are encouraged to apply: effects of climate change on geosystems, resiliency of civil infrastructure systems across multiple geohazards, sensing technologies in geoengineering, biomediated and bioinspired geotechnical engineering, energy-related applications of geotechnical engineering and artificial intelligence/machine learning applications to geotechnical engineering.
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As a Quantitative Analyst 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 cloud 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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The areas include but are not limited to machine translation, translation memory, information extraction, name-matching, speech/audio processing, document exploitation, and evaluation. Preferred Qualifications Bachelor’s or Master’s preferred in computational linguistics, artificial intelligence, computer science, or mathematics.
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Expert knowledge of appropriate analytic methods and methodological tools in one or more of the following areas: Applied Mathematics (e.g. probability and statistics, formal modeling, computational social sciences); Computer Programming (e.g. programming languages, math/statistics packages, computer science, machine learning, scientific computing); and Visualization (e.g. GIS/geospatial analysis, telemetry analysis.
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We integrate off-the-shelf and new development efforts to sustain and enhance Defense Intelligence Agency’s (DIA) National Media and Exploitation Center (NMEC) architecture 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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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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At Capital One, we believe that Machine Learning (ML) represents the biggest opportunity in financial services today, and is a chance to revolutionize the industry. Capital Ones commitment to Machine Learning has sponsorship from leadership and the Enterprise ML Program is at the heart of this effort, and is leading the way towards building responsible and impactful tools, platforms, and solutions that leverage ML and Generative AI.
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A deep technical background in applied data science, spanning some or all statistics, experimentation, machine learning, causal methods, optimization techniques, data engineering and architecture (applied to large data), and behavioral analytics.
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Integrate, configure, and implement: machine learning (ML) and artificial intelligence (AI) technologies; platform as a service (PaaS) capabilities to include containers and container technologies; authorization, authentication, single sign-on, and identity management capabilities to include Identify, Credential and Access Management (ICAM); geospatial mapping capabilities and services; and data analytics capabilities.
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machine learning jobs Title: principal Company: Commscope in Washington, DC
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