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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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Proficient in project software such as SAS/SQL, Python/PySpark, Snowflake, R, Databricks CMS cloud environments. Builds complex models for specific analysis using technology such as Python (Python, PySpark), R, SQL, SAS, Databricks.
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Basic working knowledge of SQL, SAS, R, Python, or other computer language (Associate) 1+ years professional experience with SAS, SQL, R, Python or other statistical programming language (SAS/SQL preferred) (Mid Level.
$118,500 a yearFull-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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65% - Data Analytics - Analyzes large volumes of data and leverages data analytics tools to create new dashboards, reports and any additional ad-hoc requests, using a wide variety of database applications / analytical tools /languages including GCP BigQuery, SQL, SAS, R, Python, Jupyter Notebook, Adobe Analytics, and Tableau.
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Shows proficiency in development, implementation, and rollout of statistical and deep learning tools and techniques (e.g. using R, R-shiny, Python, Julia, etc.) Proficient in Python, Pytorch, Tensorflow, Keras and Githubs with deep learning experience in RNN, CNN, NLP or imaging is a plus.
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Responsible for acquiring, accessing, validating, querying, and analyzing data from data repositories using available tools (including Alteryx, Python, R, SQL, Power BI, or Microsoft Azure applications.
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As an Associate Data Scientist at IBM, you will work to solve business problems using leading-edge and open-source tools such as Python, R, and TensorFlow, combined with IBM tools and our AI application suites.
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Proficiency in analytical software and tools including Python, SAS, R, SQL and other applications, such as Microsoft Excel/VBA and Access; Demonstrating proficiency in analytical software and tools including Python, SAS, R, SQL and other applications, such as Microsoft Excel/VBA and Access.
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Model documentation and/or model validation report writing; and Visualization techniques for summarizing complex financial data analyses; Statistical and machine learning techniques for analyzing financial data, including survival analysis, time series analysis, GLM, logistic regression, XGboost, LightGBM, Random Forest, Neural Networks, decision tree analysis, and cluster analyses.
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Statistical techniques for analyzing financial data, including sampling, optimization, logistic regression, linear regression, decision tree analysis, and cluster analyses; We focus on a collection of organisational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organisations in order to keep up with the changing nature of customers and technology.
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We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge. As part of our team, you'll help our clients implement enterprise content and data management applications that improve operational effectiveness and provide impactful data analytics and insights.
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A career within Data and Analytics services will provide you with the opportunity to help organisations uncover enterprise insights and drive business results using smarter data analytics.
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Use data and insights to inform conclusions and support decision-making. Specialty/Competency: Data, Analytics & AI. Applying expertise in multiple modeling methodologies including survival analysis, transition rate modeling, time series analysis, credit scoring, Monte Carlo simulation, Artificial Intelligence/Machine Learning techniques such as neural networks and tree based algorithms etc.
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Experience leveraging complex data to drive business decisions, hands-on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Python, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL. Programming Languages: Python, R, SQL, Java or Scala, SQL. Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
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r python jobs Title: data scientist computer vision engineer
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