What are the responsibilities and job description for the Data Scientist I position at NR Consulting LLC?
Description:
Looking for talented Data Scientists who have experience building data-based solutions powered by the advancement of Machine Learning algorithms and Deep Learning networks. As a Data Scientist (Stars Business Intelligence, Medicare) you will play a critical role in developing analytical capabilities to improve and optimize member experience.
Responsibilities:
As a Data Scientist you:
· Should have the ability to communicate data insights to all organizational levels, concluding, defining recommended actions, and reporting results across stakeholders.
· Should work on integrating data from different data sources.
· Should be working on pre-processing large datasets to build machine learning models, automating, deploying, and maintaining them into production.
· Should be able to understand how the deployed models run correctly.
· Should develop, test, and deploy data structures using Entity-Relationship Diagramming, and data modeling tools.
Requirements:
· 1 years of hands-on experience on Flask and Rest-API, model deployment.
· 3 years of hands-on experience with Python, MySQL, and SAS (SAS Enterprise), R, Tableau, SPSS, STATA.
· 5 years of experience in data science specialization, including statistical data analysis and/or machine learning in an enterprise-scale environment.
· Deep understanding of common database technologies, such as SQL Database/Server, SQL Data Warehouse, Oracle, DB2, Netezza, MySQL, and other data sources, such as Azure Data Lake Storage and Azure Blob Storage.
· Experience working with distributed computing tools (Hadoop, Hive, Spark, etc.)
· Expert in Docker, CI/CD deployment, writing YMAL files to implement code and functions as service.
· Experience with Cloud Platforms using GCP/Azure/AWS.
· Hands-on experience with real-time streaming processing as well as high volume batch processing, and skilled in Advanced SQL, Amazon S3, Apache Kafka, Data-Lakes, etc.· Experience with Tableau is a plus.
· Experience with large scale data mining tools such as Spark
· Advanced understanding of best practices for structuring and organizing Data Lake file systems for large volumes of data.
· Experience with Client models automation and deployment to production.
· Experience performing advanced data pipelines, data structure and modeling, data processing, data extraction, joining, manipulation cleaning, analysis, and presentation for medium to large datasets.
· Experience developing models for forecasting, classification, clustering, regression analysis, recommendations, variable selections, and natural language processing.
· Experience with scientific computing and analysis packages such as NumPy, Pandas, Scikit-Learn, SciPy, and ggplot2.
· Experience with Deep Learning frameworks like PyTorch, TensorFlow, and Keras.
· Experience with automated feature engineering/feature extraction and reduction.
· Experience with data visualization libraries such as Matplotlib, Seaborn Pyplot, ggplot2.
· Strong grasp of experimental design, A/B testing, and advanced statistical analysis
· Experience with Git, GitHub, and Linux administration.
· Experience leading end-to-end data science project implementation including training, testing, and deploying machine learning models in production environments.
Looking for talented Data Scientists who have experience building data-based solutions powered by the advancement of Machine Learning algorithms and Deep Learning networks. As a Data Scientist (Stars Business Intelligence, Medicare) you will play a critical role in developing analytical capabilities to improve and optimize member experience.
Responsibilities:
As a Data Scientist you:
· Should have the ability to communicate data insights to all organizational levels, concluding, defining recommended actions, and reporting results across stakeholders.
· Should work on integrating data from different data sources.
· Should be working on pre-processing large datasets to build machine learning models, automating, deploying, and maintaining them into production.
· Should be able to understand how the deployed models run correctly.
· Should develop, test, and deploy data structures using Entity-Relationship Diagramming, and data modeling tools.
Requirements:
· 1 years of hands-on experience on Flask and Rest-API, model deployment.
· 3 years of hands-on experience with Python, MySQL, and SAS (SAS Enterprise), R, Tableau, SPSS, STATA.
· 5 years of experience in data science specialization, including statistical data analysis and/or machine learning in an enterprise-scale environment.
· Deep understanding of common database technologies, such as SQL Database/Server, SQL Data Warehouse, Oracle, DB2, Netezza, MySQL, and other data sources, such as Azure Data Lake Storage and Azure Blob Storage.
· Experience working with distributed computing tools (Hadoop, Hive, Spark, etc.)
· Expert in Docker, CI/CD deployment, writing YMAL files to implement code and functions as service.
· Experience with Cloud Platforms using GCP/Azure/AWS.
· Hands-on experience with real-time streaming processing as well as high volume batch processing, and skilled in Advanced SQL, Amazon S3, Apache Kafka, Data-Lakes, etc.· Experience with Tableau is a plus.
· Experience with large scale data mining tools such as Spark
· Advanced understanding of best practices for structuring and organizing Data Lake file systems for large volumes of data.
· Experience with Client models automation and deployment to production.
· Experience performing advanced data pipelines, data structure and modeling, data processing, data extraction, joining, manipulation cleaning, analysis, and presentation for medium to large datasets.
· Experience developing models for forecasting, classification, clustering, regression analysis, recommendations, variable selections, and natural language processing.
· Experience with scientific computing and analysis packages such as NumPy, Pandas, Scikit-Learn, SciPy, and ggplot2.
· Experience with Deep Learning frameworks like PyTorch, TensorFlow, and Keras.
· Experience with automated feature engineering/feature extraction and reduction.
· Experience with data visualization libraries such as Matplotlib, Seaborn Pyplot, ggplot2.
· Strong grasp of experimental design, A/B testing, and advanced statistical analysis
· Experience with Git, GitHub, and Linux administration.
· Experience leading end-to-end data science project implementation including training, testing, and deploying machine learning models in production environments.
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