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Deep Learning: Understanding neural networks, backpropagation, architectures like CNNs, RNNs, transformers, and tools like TensorFlow, PyTorch, or Keras. Linear Algebra: Understanding of deep learning algorithms.
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Leverage modern ML/NLP methodologies (e.g., deep learning), computing hardware (e.g., GPUs), cloud infrastructure (AWS/Azure), open-source modeling frameworks (e.g., TensorFlow, Keras, PyTorch, XGBoost.
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As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock. This role is for a Director of Regulatory Affairs on the US Government Affairs & Public Policy team with a legal background and deep expertise in securities law and experience in the financial services market.
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Demonstrated professional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras. Stay up-to-date with the latest trends and advancements in machine learning and AI technologies The ideal candidate will have a strong background in machine learning, deep learning, and data science.
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The Servicing Intelligence data science team builds deep learning models to help our internal customers use unstructured, multi-modal data sources — text, image, and audio data. At least 2 years of experience working with deep learning libraries and tools, such as Pytorch and HuggingFace.
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You have experience with clustering, classification, sentiment analysis, time series, and deep learning. 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.
$184,800 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Minimum of 4 years of experience in designing and developing neural network models, especially deep learning model, and Foundation models. + Minimum of 4 years of experience in deep learning and neural networks, in particular training and large language models with popular libraries and GPUs.
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The Leidos EW Division performs on programs that include advanced signal processing, machine learning, optimization, detection & estimation, deep learning, and adaptive decision and control.
$65,000 - $117,500 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Our consultants bring deep expertise in Data Science, Machine Learning and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data.
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Large scale machine learning & deep learning models. deep learning, reinforcement learning, and computer vision techniques. Data science, machine learning, optimization models, Master’s degree 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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Expertise in advanced machine learning techniques, including deep learning, data mining, clustering, regression models, predictive modeling, optimization, and ranking. Proficiency in ML and Deep learning libraries in Python (MLlib, PyTorch, Keras, TensorFlow, etc.
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Familiarity and/or willing to learn deep learning capabilities leveraging Tensorflow/Torch. ALTA IT Services is seeking a Machine Learning Engineer to join our client on a multi year contract basis.
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The Enterprise OneStream RunTime team in the Enterprise Data and Machine Learning (EDML) organization supports the AWS hosted Data Ingestion and Data Processing applications enabling a centralized data movement platform across Capital One. You will get an opportunity and exposure to all possible data pattern combinations across Capital One and dive deep into the unique engineering challenges it brings around data integrity, security and scale.
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At least 4 years of hands-on experience in several of the following areas: machine learning, statistics, optimization, regression modelling, deep learning, generative AI, causal inference, experimentation.
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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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deep learning jobs in Mclean, VA
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