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These new experiences are powered by a large-scale deep learning-based enterprise search and question-answering systems built by the team, which processes billions-to-trillions of data items and serves millions of end users in millisecond latency.
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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.
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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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From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
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Keep up with rapidly evolving technologies like Machine Learning, artificial intelligence, and Natural Language Search (NLS) to ensure our Core Experience products meet the needs of our customers and are delivered quickly to capture user feedback and insights to learn and iterate.
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7+ years of industry experience building production machine learning systems at scale, data mining, search, recommendations, and/or natural language processing. Design features and build large-scale machine learning models to improve user ads onsite engagement prediction.
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End-to-end hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (e.g., Hadoop/Spark) 2+ years of industry experience applying machine learning methods (e.g., user modeling, personalization, recommender systems, search, ranking, natural language processing, reinforcement learning, and graph representation learning.
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As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies.
$133,000 - $194,000 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Define the data structure, framework, design, and evaluation metrics for research solution development and implementation under minimal guidance. Responsibilities Author research papers to share and generate impact of research results across the team and in the research community.
$133,000 - $194,000 a yearFull-timeExpandApply NowActive JobUpdated 3 months ago - UpvoteDownvoteShare Job
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Experience in one or more of the following areas: NLP, Ranking, Ads, search engine, recommender system, distributed system, and machine learning. Familiar with NLP, CV-related algorithms, technologies, and familiar with large-scale model training and RL algorithms are preferred.
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Excellent coding ability, data structures, and fundamental algorithm skills, proficient in C/C. Data construction, instruction tuning, preference alignment, and model optimization; Exploring and developing large-scale language models and optimizing enterprise applications to the extreme.
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Responsible for the research and application of the company's large-scale models, exploring new applications and solutions for related technologies in the fields of search, recommendation, advertising, content creation, and customer service, to meet the growing demand for intelligent interactions from users and comprehensively improve their way of life and communication in the future world.
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Search for ways to automate and scale time series data solutions across the business. Working knowledge of machine learning application (eg. - Perform exploratory data analysis in search of new data solutions.
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Drive the team to leverage the latest techniques in data engineering, machine learning, and data visualization to build a suite of both user-facing and internal-facing capabilities that enable fast resolution of customer issues to keep the customers focused on getting the most value out of the Snowflake platform.
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Snowflake has a vast amount of diagnostic and machine data that the team will use to create large scale batch and streaming data pipelines, visualizations, and applications to extract insights and intelligence from to get customers past roadblocks and back to consuming the Snowflake platform.
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