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We work alongside product teams across MI ES on break-through ideas using tools and techniques spanning the entire spectrum of Data Science, Statistics, Machine Learning, Deep Learning, NLP, Gen AI, Operations Research, Data and Machine Learning Engineering.
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Deep knowledge in Machine Learning, Deep Learning, Data Mining, Information Retrieval, Statistics. Machine Learning Scientist – Quant AI - Vice PresidentThe Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems.
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The Data & Analytics department in Legal and Compliance is responsible for designing and optimizing surveillance models, approaches and tools using advanced analytical techniques like supervised and unsupervised machine learning, Natural Language Processing (NLP) and evolving techniques like reinforcement and deep learning as well as graph analytics.
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The ideal candidate will have a strong background in computer vision, machine learning, and image processing. Work with large datasets, including video and image data, to train and validate machine learning models.
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5 years of experience with machine learning algorithms and tools (e.g., TensorFlow), artificial intelligence, deep learning, or natural language processing. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day.
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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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Audible Content Data Science team partners with business, technology, and product leaders to solve problems related to Content Understanding, Content Evaluation, and Recommendations, relying on Natural Language Processing (NLP), ML, Deep Learning, LLMs/GenAI. We operate in an agile environment in which we own the life cycle of research, design, model development and, often, deployment.
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NLP Competence: Deep understanding of Natural Language Processing methods required for analyzing unstructured text data. Develop and/or leverage leading-edge statistical and machine-learning models to enhance the research and development systems.
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As a Product Manager, Computational Linguist on the Machine Learning & Optimization team, you are an integral part of the group that optimizes features, models and AI capabilities for the Chase Digital Assistant and other conversational AI products.
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Solid understanding of AI/ML algorithms and techniques, including deep learning, time series forecasting and natural language processing. Continuous Learning: Stay updated with the latest trends and advancements in data science, machine learning, and related fields, and actively seek opportunities to enhance skills and knowledge.
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Develop and apply statistical models, machine learning algorithms, and deep learning techniques to solve complex problems. Stay up-to-date with the latest advancements in data science and machine learning.
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Leverage machine learning, information retrieval and big data processing technologies to empower ranking, recommendation, and content experiences. Experience with TensorFlow, PyTorch, or related deep learning frameworks.
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We advance brand understanding to facilitate better brand shopping and brand building – we do this through innovations in natural language processing, deep learning, graph science, and computer vision.
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Strong computational and programming skills Familiarity with advanced machine learning and deep learning applied to large datasets. Job Responsibilities: Analyze clinical data using machine learning techniques to support research outcomes.
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Technical Expertise: A deep understanding of Enterprise Software architecture, design patterns, and modern programming languages coupled with a strong foundation in machine learning algorithms, data modeling, and MLOps practices across the major cloud providers (AWS, Azure, GCP.
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