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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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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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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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Minimum Certifications: Certified Registered Central Service Technician - CRCST (IAHCSMM) and/or Sterile Processing Technician certification. Sterile Processing Technician cleans, disinfects and sterilizes instruments, equipment, surgical linen and supplies.
$19ExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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Stay ahead of emerging technologies and methodologies in data engineering and machine learning. Metropolis is seeking a Senior Machine Learning Data Engineer to play a crucial role in architecting, implementing, and managing our ML data ecosystem to support advanced machine learning initiatives.
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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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You will develop robust and scalable data pipelines, ML model hosting, and backend services for enterprise line of business applications in Python, VertexAI, Airflow, dbt, Flask, PostgreSQL, BigQuery, Docker, Kubernetes, and more.
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Hands on experience with Modern Data Warehousing in cloud, Azure Data Factory, Azure Databricks, Pyspark, Azure DevOps, Azure Machine Learning, Azure Data Lake, Power BI, MS SQL Server, SSAS, SSRS, SSIS, Tableau, Alteryx, Natural Language Processing, Scikit-Learn (Classification, Clustering and recommendation system.
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Hands-on experience with analytics and big data technologies within Microsoft Azure, with experiences in tools such as Azure Data Factory, Azure Machine Learning, Azure Cognitive Services, Azure Databricks and Azure Synapse Analytics.
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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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Proficiency in programming languages such as R, Python, and SQL, with experience in data analysis and machine learning tools like PyTorch, Pandas, scikit-learn, Tensorflow, or similar.
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As needed, collaborate with internal and external stakeholders to identify object detection, optical character recognition, automation, predictive modeling, pattern analysis, natural language processing, fraud detection, and other business cases for using ML.
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Expertise in the physics of additive manufacturing processes such as wire arc DED and laser powder DEDExpertise in physics-based simulation and its integration with machine learning models.
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Have some experience in multiple modalities of deep learning, such as computer vision, natural language processing or audio. Strong technical background in machine learning and deep learning.
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Experience with Artificial Intelligence/Machine Learning (AI/ML) applications for image processing. Develop image processing algorithms for Electro-Optical/Infrared (EO/IR) or Hyperspectral Imagery (HSI) data.
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