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Techniques: Statistical Analysis, Visualization, Optimization, Machine Learning, Big Data, Data Warehouse, NLP. Advanced Analytics - Applied Econometrics, Advanced Calculus, Statistical Methods, Data Visualization, Decision Analytics & Optimization, ML, Big Data, Social Network Analytics.
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Experience in any cloud data warehouse like Synapse, Big Query, Redshift, Snowflake. Optimize Databricks jobs for performance and scalability to handle big data workloads.
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Proficiency in Databricks for distributed data processing, analytics, and machine learning on big data. Proven experience as a Data Engineer with a strong emphasis on data virtualization, SQL programming, DevOps practices, Python, and Databricks.
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Candidates will be considered from any specialization within surface processes/geomorphology; those focusing on coastal processes, natural hazards, or the cryosphere, and who use tools such as numerical modeling, remote sensing, and big data science are especially encouraged to apply.
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Experience utilizing modern data infrastructure tools such as cloud data warehouses (Snowflake, BigQuery, etc), data integration tools (Fivetran, Airflow, etc), business intelligence tools (PowerBI, Sigma, etc), CRM systems (Salesforce, Everyaction, etc), and other enterprise data tools (such as Airtable) required.
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High familiarity with data platforms and applications such as: Databricks, Jupyter, Snowflake, Redshift, Airflow. Familiarity with data exploration and data visualization tools like Tableau, Looker, Chartio, etc.
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Experienced in using Big Data stack (Hadoop, Hive, Spark, Kafka, Airflow/OOZIE, BigQuery/Presto/Impala, etc.) You have an interest in growing your knowledge in the area of Data Science and are willing to lend a hand in "Machine Learning" application development when required.
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Define data mapping and lineage to produce accurate reconciliations across multiple sources. The candidate will also be responsible for data mapping and lineage definition in order to produce accurate reconciliations across multiple sources.
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As a Security Engineer at Bilt Rewards, you will be entrusted with the critical task of protecting sensitive company data, responding promptly to potential breaches, and implementing robust security and data governance protocols.
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Some exposure to Big Data technologies, Apache Spark, AWS, AWS S3, amazon Glue, cloud -based data architecture is preferred. Experience with any of the Data Governance tool – such as Collibra, Alation, Informatica Axon DG, or Apache Atlas.
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Promote modern approaches in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Large Language Models (LLMs) within the computational and data ecosystem to advance researcher productivity and scientific discovery.
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Experience in one of the public cloud providers – GCP, Azure, AWS.Experience with relational SQL and NoSQL databases, including Postgres and MongoDB.Experience with workflow management tools: Airflow, AWS data pipeline, Google Cloud Composer, etc.
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3 year of experience in Banking industry specifically in Financial Crimes Data Governance for Sanction Screening, Anti Money Laundering and KYC. Will closely work with Chief Data Office (CDO) and Financial Crimes Data Officer teams to handle the data analysis requests, internal controls, performance tuning, impact analysis requests.
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Conceive, plan and prioritizing data projects with the Director of Data Science & Machine Learning. Document classification and extraction: Data scientists can use natural language processing (NLP) techniques to classify and extract information from documents, such as news articles and social media posts.
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Design and implement complex data models on Azure Synapse Analytics to support data warehousing and big data solutions. Experienced in big data and NoSQL solutions by developing scalable data processing platforms to drive high-value insights to the organization.
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big data jobs Title: machine learning engineer Company: Home Depot in New York, NY
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