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Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
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Machine learning will cover everything from data science, Artificial intelligence, business analytics, deep learning, and computer science. Difference between Machine learning, Artificial Intelligence and Deep learning.
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Experience may be gained concurrently and must include one (1) year in each of the following:-Building statistical models and machine learning models using large datasets from multiple resources-Working with Customer, Content, or Product data modeling and extraction-Using database technologies such as SQL or ETL-Applying specialized modelling software including Python, R, SAS, MATLAB, or Stata.
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Lead the standardization of process automation solutions leveraging advanced technologies such as robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML.
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Is required (test required)Algorithmic trading experience is required (essential), whether personal or professional experience Desirable Skills: Prior experience as a Quantitative Trader/ ResearcherExperience with Machine Learning, Natural Language Processing or Deep LearningKnowledge of the DeFi and Web 3.0 ecosystem.
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You have successfully partnered with Product, Data Engineering, Data Science and Machine Learning teams on strategic data initiatives. Modernize Signifyd’s Machine Learning (ML) Platform to scale for resiliency, performance, and operational excellence working closely with Engineering and Data Science teams across Signifyd’s R&D group.
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4- 8 years of experience as machine learning engineer/data scientist. Experience using machine learning libraries or platforms, including Tensorflow, Caffe, Theanos, Scikit- Learn,or ML Lib for production or commercial products.
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LLM PhD focus on NLP or Masters with 5 years of industrial NLP research experience Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization) Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Publications in deep learning theory Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR.
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PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields. Optimization (Training & Inference) PhD focused on topics related to optimizing training of very large deep learning models Multiple years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression Experience optimizing training for a 10B+ model Deep knowledge of deep learning algorithmic and/or optimizer design Experience with compiler design.
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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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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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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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The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. Design, prototype, implement, evaluate, optimize and monitor machine learning algorithms and related software systems to generate sports datasets and predictions with high accuracy and low latency.
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3-5 years of experience building and implementing artificial intelligence, neural networks, deep learning, or machine learning capabilities in software applications in a national security or academic environment.
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From there, we develop and integrate data technology solutions that harness the latest in data visualisation and machine-learning, and provide digital marketing and rights-sales services that grow revenue across ticketing, premium, sponsorship and media.
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machine learning deep jobs Company: Services Llc in New York, NY
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