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Minimum 9 years of experience with big data technologies IE Hadoop, Apache Spark, and/or Databricks. Minimum 9 years of experience with big data technologies IE Hadoop, Apache Spark, and/or Databricks.
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Proficiency in interacting with various database and file storage systems (Examples: Oracle, Hadoop, NoSQL). Freshers are also encouraged to apply. Experience with visualization tools such as Tableau, PowerBi, or QlikView.
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Big Data Technologies: Familiarity with big data platforms and technologies such as Hadoop, Spark, and distributed computing. Data Collection: Collect, acquire, and gather data from various sources, which may include databases, APIs, web scraping, and more.
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Deep understanding of Agile methodologies and tools like Azure DevOps. Ability to understand customer requirements and translate these into solution specifications and system designs. Statistics and Mathematics: 2 + years of experience in statistical concepts, probability, linear algebra, and calculus, which are fundamental to data analysis and modeling.
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Technical experience with Python, Apache Spark, NoSQL/Hadoop, SQL, Data Visualization, Machine Learning, Artificial intelligence and more. Technical experience with Python, Apache Spark, NoSQL/Hadoop, SQL, Data Visualization, Machine Learning, Artificial intelligence and more.
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Requires 5 years of experience creating data products using HDFS and Hive within Hadoop to analyze and secure data; or building data pipelines, developing data products to support critical business initiatives, and overseeing data landing requests.
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Deep understanding of Agile methodologies and tools like Azure DevOps. Machine Learning: Apply various machine learning techniques, such as regression, classification, clustering, and deep learning, depending on the problem at hand.
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Broad expertise in machine learning frameworks (TensorFlow, PyTorch, scikit-learn) and data engineering tools (Spark, Hadoop) for large-scale medical imaging data processing. Broad expertise in machine learning frameworks (TensorFlow, PyTorch, scikit-learn) and data engineering tools (Spark, Hadoop) for large-scale medical imaging data processing.
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Data Ethics: Knowledge of ethical considerations in data science, including privacy, bias, fairness, and regulatory compliance. Model Evaluation: Expertise in evaluating machine learning models, conducting cross-validation, and hyperparameter tuning.
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