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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, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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Able to provide architecture, design, implementation and operationalization of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse. Able to develop EL pipelines in and out of data warehouse using combination of Databricks, Python and SnowSQL.
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Platforms including but not limited to: Instrumentation, PLC, DCS Controllers, HMI/SCADA, Server Architectures, Plant Historian, APC, Motor Control, Intelligent Power, as well as Analytics, IIoT, and Cloud Automation.
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Requires good understanding of Digital Transformation, relevant technologies, industry leaders in Sales & Services (e.g. CRM), Digital Experience Platforms, Cloud, Infrastructure Services, API Management, DevOps, Data/Analytics, Omni Channel Solutions, RPA/Automation, Customer Experience, UX, Managed Services, Application Services.
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Data Engineering & Analytics: Maximizing data in its raw format and elegantly engineering, transforming, modeling, visualizing, and streaming it into the fabric of modern products and outstanding customer experiences.
$22.75 - $29 an hourFull-timeExpandApply NowActive JobUpdated 3 months ago - UpvoteDownvoteShare Job
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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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Good understanding of Search Engine Optimization (SEO) principles and ensuring that application will adhere to them. Performs data analysis and data modeling. Integrates data from various back-end services and databases.
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Experience with Cloud based data architectures, messaging, and analytics. Demonstrated proficiency with Data Analytics, Data Insights. 5+ years with Data Warehouse Architecture, hands-on experience with Databricks platform.
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Keywords: ETL sql ssis informatica dwh data warehousing banking financial azure snowflake databricks ELT market risk jira confluence tidal django flask pyramid tornado pytest pylint. Direct experience of building Data Pipelines using Azure Data Factory and (preferably Databricks.
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Prepares asset files including equipment data sheets, impact assessments, P&ID & drawings, factory O&Ms, factory maintenance recommendations, maintenance work plans and procedures. Generates SOPs and Work Practices within Celgene's Document Management System and electronic work plan template and measurement data templates to establish the Use, Maintenance and Calibration plans for GMP equipment.
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Verify inventory systems data (WMS, Cloud Analytics, Netsuite, Looker) to manage warehouse materials & ensure capacity limits are adhered to. The lead serves as a process compliance auditor, identifying errors in physical and digital processes, collecting data on movement & inventory accuracy, and providing the Analyst & team leadership with direct feedback and observations from the floor.
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Experience building data piplines using Azure Data Factory and Databricks (good to have) ETL developers are responsible for designing and creating the data warehouse and all related extraction, transformation and load of data functions.
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The Clinical & Real-World Evidence Generation (CRWEG) Value Team brings together product, engineering, and data expertise to drive the data and technology strategy that accelerates our company's ability to develop innovative medicines that change the course of human health.
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Bachelor's Degree in Engineering or other technical field (preferred studies included Thermodynamics, Hydronics, Psychometrics, Environmental or Facility Operating) and 5+ years in building systems/operations or 10+ years proven track record of performance in Mechanical Systems data collection/operation & building systems or operations.
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Cloud-based data & analytics frameworks (e.g., Azure, Databricks, Snowflake) Create strong partnerships with various teams across PVH and be a thought partner on concepts that will lead to evolving PVH data & analytics maturity and grow the respective portfolio through potential projects for prioritization by the business.
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