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Familiarity in relevant machine learning frameworks and packages such as Tensorflow and PyTorch. PayPal is seeking a talented Staff Machine Learning Scientist join our Global Machine Learning team, driving the development of AI-driven solutions that will shape the future of PayPal. You design and develop ML solutions including personalization, recommendation, and ranking of deals and promotion across our Brands and surfaces including BrainTree, Venmo and PayPal. Your goal will be to optimize the customer journey through personalized, thrilling experiences.
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Organization Overview The newly-created Data Science Resource Center (DSRC) is responsible for supporting and advancing the research objectives of Rockefeller University's faculty and staff by providing training and consultative support in the field of data science - especially in the areas of machine learning and artificial intelligence (ML/AI.
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About You:We are looking for a passionate Machine Learning Engineer to build and scale the DTC personalization systems and services for our new global streaming app, Max, as well as any future DTC streaming apps.
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Desired Skills: Demonstrated experience in delivering Machine Learning & Artificial Intelligence use cases for Banking/ Finance industry, a plus. General knowledge of process improvement frameworks (Lean, Value Stream Mapping) and or exposure to process management and automation tooling (Celonis, UiPath, Camunda.
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With more than 1 Million+ models and 320K+ stars on GitHub, over 15.000 companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Grammarly and NASA. About the Role As an open-source Machine Learning Engineer, you will work to improve the open-source machine learning ecosystem.
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3+ year(s) of work experience in a university, industry, or government lab with emphasis on AI Research in machine learning, deep learning, and/or natural language processing. Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU.
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Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types.
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We host and sponsor London’s PyData and Machine Learning MeetupsWe open-source some of our technology. We are also committed to continuous learning and development via coaching, mentoring, regular conference attendance and sponsoring academic and professional qualifications.
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Experience building loan-level credit/prepayment models from data preparation, data analysis, and model estimation through deployment into productionExperience with generalized regression models as well as machine learning frameworksVery strong programming and software design skills (Python, C.
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Experience in Data science and Machine learning using Python, R, Java, C#, Spark, AutoML, TensorFlow, Amazon AML, Microsoft machine learning studio, PyTorch, IBM Watson and any graph DB.
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Understanding of strengths and weaknesses in application of Machine Learning and Artificial intelligence to applications in Transaction monitoring and Sanctions screening. Director, Model Validation for SHUSA Compliance models (Transaction Monitoring, Sanctions Screening, Fraud Risk, etc) will be responsible for leading the independent validation of models used by various Compliance groups in conformance with regulatory guidance on model risk SR- and other regulatory requirements around Compliance models.
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Work Experience with Product Management, Machine Learning, Deep Learning, Generative AI, Large Language Models, Natural Language Processing. Scale is at the forefront of enabling Machine Learning across multiple industries including Generative AI (e.g., chatbots.
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You will work with technologies like Python, Docker, Kubernetes, PyTorch, TensorFlow, and cloud platforms to write scalable and optimized data access patterns for real-time machine learning applications.
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Provide technical leadership in guiding the development and implementation of machine learning models for automated call detection (focusing on out-of-bounds calls initially), focusing on achieving low latency for real-time decision support for referees.
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AdTheorent is the leader in Demand Side Platform (DSP) machine learning solutions for advertising. Since 2011, AdTheorent's advanced machine learning and media buying platform has delivered measurable real-world value for advertisers.
$135,000 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago
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