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The team uses emerging technologies and methods such as data fabric, machine learning, GenAI, NLP, computer vision, artificial intelligence automation, big data analytics, and cloud native application development to drive innovation, operational efficiency, and provide the data product excellence our customers have come to value and trust us for.
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Energy Sciences, Biosciences, Physical Sciences, Computing Sciences, Nanotechnology, Supercomputing, Big Data, Energy Innovation, Science, Climate Change, exascale, computing, environmental science, materials science, Artificial Intelligence, AI, Machine Learning, Climate, Supercomputing, and Basic Science.
$126,900 - $214,140 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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These technologies may include artificial intelligence, machine learning, statistical techniques, mixed integer linear programs, reporting/big data analysis, dashboarding, data integration, and other heuristics/algorithms.
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Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or related technical fields or equivalent (minimum 12 years) work experience. Certifications in AI, machine learning, or data science.
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At least 2 from the following: Statistics and Probability Theory, Regression Modelling, Clustering Methods, Deep Learning, Neural Networks, Natural Language Processing, Text Mining, Computer Vision, Image Recognition, Time Series Forecasting, Machine Learning Visualization Tools, Tree Ensembles.
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System design and software implementation of proof-of-concept prototypes incorporating a variety of engineering disciplines: control theory, software development, signal/video processing, pattern recognition, machine learning, artificial intelligence, high performance computing, applied mathematics, network/web services, and visualization.
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Uses analytical rigor and statistical methods to analyze large amounts of data, extracting actionable insights using advanced statistical techniques such as data analysis, data mining, optimization tools and machine learning techniques and statistics (e.g., predictive models, lifetime value, propensity models.
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Share large language model and data science platform knowledge to support the technical relationship with Google’s customers, including product/solution briefings, proof-of-concept work, provide code samples, coordination, and prioritization of solutions impacting customer adoption of Google Cloud.
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Our diverse workforce includes skilled tradespeople; artificial intelligence, machine learning (AI/ML) experts; engineers; technologists; scientists; logistics experts; and business administration professionals.
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The ideal candidate is adept at using big data sets and applying machine learning and artificial intelligence techniques for analysis and prediction tools focused on environmental and meteorological applications.
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Certification in one or more of the following technologies preferred: Cloud, mobile, Database, Data Engineering, Data Analytics, Big Data, BI, Data Science, Machine Learning, and Artificial Intelligence.
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The Expert Data Scientist will be a champion of the latest Machine Learning and Artificial Intelligence technology stack. 5+ years experience in one or multiple Big Data or cloud environments (e.g. AWS, Google AI/ML Platform., Snowflake, Databricks.
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Apply state-of-the-art algorithms relying on knowledge of statistical modeling, machine learning, and optimization to or improve the performance/quality of existing health propensities and related data products.
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