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Big Data Technologies:Distributed Computing: Tools like Apache Spark, Hadoop. Software Engineering & DevOps:Version Control: Familiarity with tools like Git. Continuous Integration/Continuous Deployment: Knowledge of pipelines and tools like Jenkins, Travis CI.Containerization: Familiarity with Docker, Kubernetes, etc., for scalable deployment of data science applications.
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The Trauma Registrar is committed to high-quality data abstraction, meets both quality and productivity standards, detailed, and works well to ensure deadlines are met. A minimum of 2 years trauma registry data abstraction experience required.
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As a member of our Trust & Safety data science team, you’ll shape the way we approach data at Gemini by using your analytical and communication skills to work with teams across the business.
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A connected data ecosystem is a key dependency to realize critical business strategies across Research and Development, such as AI/ML and precision medicine. In partnership with enterprise data platform teams, functional technology teams and data scientists, the Sr Data Engineer Clinical Data Hub will be part of a team delivering key clinical trial data initiatives that are contributing to the advancement of a connected data vision, including the Enterprise Data Fabric (EDF.
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Your expertise in ETL, Databricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupyter Notebooks will be essential in ensuring efficient data processing and analysis. Design, develop, and implement end-to-end data pipelines, utilizing ETL processes and technologies such as Databricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupyter Notebooks.
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Lead data center core and shell due diligence and design projects (including civil, structural, mechanical, electrical, plumbing, security, and fire protection for greenfield and add/move/change projects) from start to finish, ensuring budget/schedule adherence, satisfaction of design requirements, and repeatable quality.
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Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. Experience with data warehousing using AWS Redshift, SQL Server, or Snowflake.
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Experience with cloud-based data warehouse environments (e.g., Google Cloud Platform, Snowflake, Redshift, and Azure) and ETL/ELT tools (e.g., Apache Beam, Mulesoft, and Fivetran) Proficiency in one or more business intelligence and data visualization tools - e.g., Power BI, Tableau, Looker, and Cognos.
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More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.
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The SEC’s Division of Enforcement utilizes a variety of data that contain current, historical and futures information about sensitive public company data, mutual fund data, and trade/quote data.
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Adheres to required definitions and/or guidelines for data entry into data base computer applications with 95% compliance on quarterly audits to ensure inter-rater reliability. Ensures timely and accurate concurrent and retrospective reviews of trauma patients (civilian and military) medical records for inclusion criteria, data element abstraction, and identification of complications and/or performance improvement filters for further review by the Trauma Acute Care Surgery team, peer review, and performance improvement processes.
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Experience with Cloud data lakes/storage/databases and tools such as AWS S3, Snowflake, Data Pipeline and other Cloud providers such as MS Azure. Data Governance (Metadata management, privacy (at database and reporting level.
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Proficient in programming languages such as Python, Scala, or Java, and experience with data engineering tools like Apache Spark, Apache Kafka, and Airflow. Hands-on experience with data warehousing solutions like Snowflake, Redshift, or BigQuery.
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This work involves combining deep learning and data fusion techniques with petabytes of satellite imagery (radar and optical), and billions of GPS positions from vessels, mostly from the Automatic Identification System (AIS) and Vessel Monitoring Systems.
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Skilled in data visualization and use of graphical applications, including Microsoft Office (Power BI) and Tableau; major data science languages, such as R and Python; managing and merging of disparate data sources, preferably through R, Python, or SQL; statistical analysis; and data mining algorithms.
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