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Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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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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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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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.
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Deep knowledge in Machine Learning, Deep Learning, Data Mining, Information Retrieval, Statistics. Applied AI ML opportunities are available at the VP level for our Quant AI team within the Machine Learning Center of Excellence.
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We are open to hiring candidates to work out of one of the following locations:Bellevue, WA, USABASIC QUALIFICATIONS- Ph. D. in Operations Research, Machine Learning, Statistics, Computer Science, Applied Mathematics, Operations Management, Industrial Engineering, Economics or a related field.
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Strong proficiency in programming (e.g., SQL, Python, R, MATLAB), database technologies (Oracle, mySQL, relational databases; graph databases are a plus), machine learning/deep learning (network architectures are a plus), dimensionality reduction techniques (e.g., PCA), and possible cheminformatics software suites.
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Manipulates and extracts Medicare, Medicaid, and other healthcare claims data stored in Cloud environment using appropriate software such as SAS, Snowflake, Python, R, SQL, and other software as appropriate for the task.
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Expertise in data strategy, data architecture, data engineering, advanced analytics, data science, data mining, and machine learning. Experience with a variety of BI and Analytics platforms like Power BI, Tableau, DOMO, Looker, PowerBI, Alteryx, and advanced statistical analysis tools like SAS or SSPS.
$299,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Expertise in a subset of the following: machine learning, object detection, image segmentation, computer vision, image processing, remote sensing, spectral science, neural models for autonomous vehicles/robotics, structure from motion (SfM), natural language processing (NLP), large language models, data fusion, change detection, and pattern of life analysis.
$229,000 a yearFull-timeExpandApply NowActive JobUpdated 16 days ago - UpvoteDownvoteShare Job
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Stay abreast of latest AI trends and maintain state-of-the-art knowledge in machine learning, deep learning, and computational linguistics. Experience with machine learning techniques and tools including deep learning, reinforcement learning, and unsupervised learning.
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Apply fundamental and advanced quantitative methods, including machine learning and statistical modeling, to solve complex business problems and enable data-driven decision-making (e.g., build statistical and machine learning models for trend analysis and forecasting to predict future business outcomes for a Fortune 100 company.
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Economics, Statistics, Applied Mathematics, Mathematics, Financial Mathematics, Data Processing/Analytics/Science. Possessing experience with one or more of the following key programming and data analytics tools: Python, R, SAS.
$117,000 a yearFull-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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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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Topics include but are not limited to artificial intelligence, computing theory (algorithms, complexity), data science, statistics, discrete mathematics (graph theory, combinatorics), game theory, machine learning, optimization, privacy, and security.
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