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Experience in Natural language processing (NLP), Prompt engineering, Embedding, Vector DB. They will use relevant statistical techniques, machine learning models and artificial intelligence algorithms to analyze data, design data models and derive insights to influence actions that maximize business value and effectiveness for the commercial technology segments.
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Apply various Machine Learning technologies (Deep Learning - Natural Language Processing, Autoencoder; Traditional Machine Learning - Isolation Forest, Decision Tree, Random Forest, XGBoost, & LightGBM, advanced analytics (Python, SQL, and AWS) and visualization techniques to extract and analyze various datasets across the company.
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Data Scientists contribute to projects involving one or more of the following disciplines: Data Science, Applied or Theoretical Machine Learning, Deep Learning, Deep Reinforcement Learning, Physics Inspired ML, Classification Systems, Natural Language Processing, Artificial Intelligence, or Computer Vision.
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At Skit, I worked mostly on generating informed content on the topics of NLP (Natural Language Processing), AI (Artificial Intelligence) and importance of linguistics and language in building more robust Voice AI systems.
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Knowledge of Machine Learning, Artificial Intelligence, or Natural Language Processing. Knowledge of distributed data and computing tools, including MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL.
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Experience with one or more of the following: Machine Learning, Deep Learning, NLP, ranking systems, recommendation systems, backend, large-scale systems, data science, full-stack.
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Machine learning or deep learning algorithms, natural language processing techniques. + Apply statistical, machine learning, and NLP techniques to identify patterns, extract meaningful insights, or develop new tools or solution for the business stakeholders.
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Expert knowledge of at least one data science programming language (e.g. Python, R, Julia, etc.) Earned graduate degree (Ph. D. or M.Sc. + 2 years relevant industry experience) in a highly quantitative discipline (e.g., machine learning, statistics, computer science, applied mathematics, theoretical physics, physical chemistry, econometrics, bioinformatics.
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Experience with data warehousing and high speed data analytics processing frameworks like Apache Spark, Kafka, Redshift etc. Research, Teaching, and Learning (RTL) at UC Berkeley is looking for an experienced data engineer with a data science background to be part of a new initiative supporting Learning Analytics.
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Develop image processing algorithms for Electro-Optical/Infrared (EO/IR) or Hyperspectral Imagery (HSI) data. Experience with Artificial Intelligence/Machine Learning (AI/ML) applications for image processing.
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Experience working with cloud or on-prem Big Data/MPP analytics platforms (i.e. Clickhouse, Spark, Netezza, Teradata, AWS Redshift, Google BigQuery, Azure Data Warehouse, or similar). Solid understanding of machine learning, deep learning frameworks, neural network architectures, and the principle of neural network engines.
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Expertise in Natural Language Processing (NLP), Large Language Models (LLM), and Generative AI is essential. Design, develop, and deploy scalable data-driven solutions using Flask, integrating with Microsoft SQL Server.
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All efforts driven by the Middle School Literacy Specialist will be in service of an equitable, inclusive, rigorous, and diverse learning experience in reading, writing, listening, and speaking for all students.
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Xplor provides enterprise-grade SaaS solutions for businesses in “everyday life” verticals: Childcare & Education; Fitness & Wellbeing, Field Services and Personal Services – and a global cloud-based payment processing platform.
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Natural Language Processing (NLP): To understand the context and sentiment behind content engagement and social media interactions. NLP libraries such as NLTK or SpaCy for processing text data.
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