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Continue to develop and help drive the strategy for the organization's data strategy including data architecture, Reporting, Insights, Analytics, Operations, Predictive modeling, attribution and Machine Learning/AI.
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Experienced in using multiple data science methodologies to solve complex business problems (e.g. statistical analysis, research science, machine learning and deep learning techniques, data modeling, regression modeling, financial analysis, demand modeling, etc.
$90,000 - $120,000 a yearFull-timeExpandApply NowActive JobUpdated 11 days ago - UpvoteDownvoteShare Job
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Strong knowledge of statistical analysis, predictive modeling, machine learning, and optimization techniques. Demonstrated passion for continuous learning and staying updated on the latest advancements in analytics and transportation industry trends.
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Alternative education and experience required: Bachelor's degree in Data Science, Machine Learning, Software Engineering, Statistics, Operations Research, or related field and 8 years of progressive, post-baccalaureate experience as Data Analyst or Scientist, Machine Learning Engineer, Statistician, Business Analyst, or related role.
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We are seeking a Machine Learning Software Engineer with a history of contributions to commercial optimization and deployment projects. and/or TensorFlow/Py Torch 3 years of experience coding on models that implement natural language translation, image recognition, and sequence to sequence deep learning models.
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The Senior Lead Data will be responsible for partnering with Technology, Machine Learning, Product Architecture, and other Capital One teams to support the development of the Finance Tech Data Strategy into products and services we create and consume.
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Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across dating, personalization, growth, discovery, ads and search. Be an expert in matching algorithm and recommender systems machine learning models and own the implementation of dating recommendations in Grindr.
$160,000 - $220,000 a yearFull-timeExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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The Financial Crimes Data Science Manager is primarily responsible for leading the development and validation of predictive and machine-learning models for specific business needs using statistics, advanced mathematical techniques, and/or computer science.
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We're seeing candidates who are excited at the prospect of unlocking the secrets held by a data set, and you're fascinated by the possibilities presented by advanced data modeling, machine learning, and artificial intelligence.
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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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The Data Scientist will be responsible for leveraging data, advanced quantitative modeling, financial analysis and cloud-based machine learning technology to enable data-driven strategic decision making.
Full-timeExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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Experience with machine learning, natural language, and statistical analysis methods to include classification, collaborative filtering, association rules, sentiment analysis, topic modeling, time-series analysis, regression, statistical inference, and/or validation methods.
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High proficiency in statistical analysis, predictive modeling, machine learning algorithms, and data mining techniques. Develop and evaluate statistics and cutting-edge machine learning algorithms & models to make data analysis more efficient.
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Provide data modeling, mining, pattern analysis, data visualization and machine learning solutions to address customer needs. The position includes: analytic computing, data analytics, Machine Learning/predictive modeling and communication of solutions and results.
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Algorithm Development (25%) Implement machine learning, statistical, data-mining, bioinformatics/clinical informatics algorithms. Design, setup and run computational experiments to evaluate and benchmark cutting-edge machine learning algorithms.
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