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The Principal ML Research Scientist will also have a hands-on roll and is expected to customize and create various machine learning algorithms to operate over multi-domain data and optimizing the performance of those algorithms on the data.
$122,200 - $220,900 a yearExpandApply NowActive JobUpdated 13 days ago - UpvoteDownvoteShare Job
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Hands on experience with deep learning (e.g., CNN, RNN, LSTM, Transformer) - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing, neural deep learning methods and/or machine learning.
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Practical hands-on experience with technologies like Apache Hadoop, Apache Pig, Apache Hive, Apache Sqoop & Apache Spark. Preferred experience in Applied Econometrics, Statistics, Data Mining, Machine Learning, Analytics, Mathematics, Operations Research, Industrial Engineering, or related field.
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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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Deploy and optimize machine learning solutions on massive datasets using big data technologies. Have an excellent academic background with strong Hands-on Knowledge of Python OR Scala.
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You have/had hands-on experience with Python, Java or Scala and the ability to write reusable and efficient code to automate machine learning pipeline and data processes. Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Publications or active peer reviewer in related journals or conference, 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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Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, Keras, etc. Our expertise spans project management, machine learning, software engineering, data engineering, and analytics.
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Looking for hands-on data scientist and strong Python coding skill is critical, much higher priority than NLP, IoT or Deep Learning. The newly hired Data Scientist will be a key interface with stakeholders in all Marketing functions, Business Technology, Sales and Sales Operations, and our shared machine learning teams throughout the organization.
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You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. Team Description The Compliance Risk Management Analytics and Innovation team builds the machine learning models that contribute to the organization’s regulatory compliance and risk management program.
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Practical hands on experience with: NLP (especially operationalization in production models), Advanced machine learning techniques and tools (especially Tensorflow), modern Data Analytics & Visualization tools (Looker preferred.
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Hands-on experience with cloud platforms, particularly Azure and AWS, including AKS and EKS, for machine learning model training and deployment. As a Senior Data Scientist in our Machine Learning and AI team, you will play a key role in developing and implementing advanced machine learning models and algorithms with a specific focus on computer vision technology.
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A minimum of five years of hands-on experience as a machine learning engineer or data scientist. A minimum of three years of hands-on experience as a machine learning engineer or data scientist.
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As a Data Scientist in the Garage, 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 large number of customer records to harness the power of AI in unlocking the big opportunities to improve customer experience.
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Hands-on experience deploying and operating applications using IaaS and PaaS on major cloud providers, such as Amazon AWS, Microsoft Azure, or Google Cloud Services. Applied Machine Learning experience (regression and classification, supervised, and unsupervised learning.
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Understanding of machine learning algorithms, statistical analysis, and predictive modeling applied to real-world business use cases. ML Modeling : Build and evaluate statistical and machine learning models to support multi-node fulfillment objectives, inventory optimization, and forecasting.
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