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Experience with Machine Learning cloud technologies such as Azure Machine Learning (AML), AWS SageMaker or Spark ML. Experience with the Azure Ecosystem (Azure Data Lake, Azure Data Factory, Azure Databricks, Azure Machine Learning (AML), Azure Cognitive Services, Azure Storage.
$66,000 - $185,000Full-timeExpandApply NowActive JobUpdated 5 days ago - UpvoteDownvoteShare Job
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Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow. Master's Degree in related field (e.g., Data Science, Predictive Analytics, Machine Learning, Statistics, Applied Mathematics, Computer Science.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
$132,000 - $264,000 a yearFull-timeExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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Design, build, test and maintain the Machine Learning platform supporting Data Science initiatives. QualificationsExperience with end-to-end Machine Learning Engineering lifecycle.
$66,000 - $185,000Full-timeExpandApply NowActive JobUpdated 5 days ago - UpvoteDownvoteShare Job
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Strong understanding of machine learning algorithms, techniques, and frameworks, including deep learning, neural networks, and ensemble methods. Experience with building and training machine learning models using tools like TensorFlow, Keras, or PyTorch.
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Possess direct experience and stay at the forefront of latest trends in machine learning, statistical test design, media mix modeling, artificial intelligence, and other data science fields.
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We are seeking a Senior Machine Learning Engineer to join DS&E. Candidates should possess robust knowledge and hands-on experience in end-to-end ML modeling, including design, development, deployment, monitoring, and maintenance, plus proficiency with cloud computing infrastructure, AWS in particular.
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Understanding of containerization technologies like Docker for packaging machine learning models and deploying them in production. Ability to design and implement end-to-end machine learning pipelines for data ingestion, processing, modeling, and deployment.
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Data science, machine learning, optimization models, Master's degree in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Supervisory experience, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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Strong software development (preferably, Python) and experience working with libraries and packages related to data manipulation, statistical analysis, visualization, and machine learning algorithms and deep learning framework.
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Knowledge of data preprocessing, feature engineering, and model evaluation techniques in machine learning projects. Expertise in open source data science technologies such as Python, R, Spark, SQL.
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8+ years of professional data engineering or software engineering experience, with a focus on heterogeneous, large-scale data processing for machine learning pipelines. As a Staff Data Engineer at Prime, you will be solving problems around data modeling, scale, integrity, denormalization, availability, warehousing, analytics, machine learning infrastructure, and the list goes on.
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Current Tech Stack: AWS, Redshift, Databricks, Python, DBT, Spark, Airflow, Kafka, Kubernetes, LightGBM, MLFlow, Metabase. Curiosity: we keep our minds open and never stop learning. Current Tech Stack: AWS, Redshift, Databricks, Python, DBT, Spark, Airflow, Kafka, Kubernetes, LightGBM, MLFlow, Metabase.
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Experience with Big Data platforms such as Databricks or Apache Spark. Experience with data streaming infrastructure deployment (e.g., Spark Streaming, Kafka, Azure EventHub, or EventBridge.
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