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What Additional Experience Makes A Strong Candidate: 4+ years of production experience with Scala 4+ years of production experience with Micro Services / Streaming Services Experience addressing ML problems, particularly in domains such as Time Series, Computer Vision (CV), Natural Language Processing (NLP), and data mining Proficiency with popular ML frameworks (Xgboost, TensorFlow, PyTorch, etc.
$220,000 - $275,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Solid ML background and familiar with standard NLU, NLG, and LLM techniquesPREFERRED QUALIFICATIONS- PhD in Computer Sciences, Electrical Engineering, or Mathematics with specialization in machine learning, deep learning, or natural language processing- 4+ years experience in building conversational AI and/or natural language processing systems.
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We process billions of mail messages using cutting edge algorithms in areas including but are not limited to: Natural language processing, GenAI, Large Language Models, Machine Learning techniques, big data processing in order of petabytes to: Extract information, build mail content and user knowledge, and interconnect different sources to identify, highlight and amplify what matters.
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Overview Are you a a data expert with broad understanding and experience in solving business problems by integrating statistical analysis, machine learning/artificial intelligence, deep learning, Natural Language Processing (NLP) and Large Language Models (LLMs.
$130,000 - $170,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Deep understanding of deep learning architecture, machine learning, natural language processing (NLP), and generative models. Design and develop solutions that utilize generative AI, leveraging Machine Learning (ML) and Large Language Models (LLM), tailored to business needs.
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Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, and/or Reinforcement Learning. Stay informed and integrate latest statistical modelling, machine learning, deep learning, recommender system and/or artificial intelligence trends to improve and enhance modelling stack and improve business metrics and customer experience.
$117,000 - $234,000 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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We are on the forefront of CBRN defense and we are looking for talented Data Scientists that have applied experience in the fields of artificial intelligence, machine learning and/or natural language processing to join our team.
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Ideal candidates have a strong publication record in areas including artificial intelligence data mining, machine learning, natural language processing, and/or fields such as cognitive science, learning analytics, and psychometrics.
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Experience with natural language processing (NLP) and deep learning frameworks TensorFlow, PyTorch). Proficiency in programming languages such as Python, R, or SQL. Strong knowledge of machine learning techniques, statistical analysis, and data visualization tools Tableau, Power BI.
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The Opportunity As a Computer Scientist/Engineer on the Fraud Team, you will be at the forefront of developing and executing an innovative strategy utilizing Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), and Multi-Agent Systems to combat a variety of fraudulent activities.
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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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Using natural language processing (NLP), machine learning (ML), Generative AI, and other relevant AI technologies and platforms; Analyzing Conversational (Chats, Emails, Messages and Calls) data, and the use of this data to build Natural Language (NLP) modeling pipelines for intent classification, training & deploying conversational AI systems, IVR, virtual assistants, chatbots etc.
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Building machine learning models and systems, interpret their output, and communicate their results to internal/client teams and how those outcomes realize business value; Using natural language understanding (NLU) and sentiment analysis.
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Understanding of Large Language Models such as Bard and/or OpenAI and NLG solutions; We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge.
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Understanding or hands on experience with Lex, Nuance Mix, Dialog Flow, AWS and Google Cloud, R, Power BI, Tableau, Alteryx; Understanding or experience with Contact Center as a service providers such as Nice, Nuance or Genesys and leading contact center transformation projects.
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