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Relevant certifications (e.g., Certified Data Management Professional, AWS Certified Solutions Architect) are preferred. Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) and big data technologies (e.g., Hadoop, Spark) is a plus.
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You have passion and expertise in one or more of the following areas: risk analytics, data analytics, machine learning, SQL, Python, cloud security, network security, vulnerability management, data protection, threat intelligence & investigations, and identity & access management.
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Data engineering skills are a plus, such as Beam, Spark, Pandas, SQL, Kafka, GCP Dataflow, etc. Develop end-to-end ML pipelines encompassing the ML lifecycle from data ingestion, data transformation, model training, model validation, model serving, and model evaluation over time.
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Developing ingestion/pre-compute jobs using container orchestration tools ( Airflow )Collaborate with the Data Science and Machine Learning teams to gather requirements, and work through implementing solutionsIterate on Mailchimp’s Prompt Management platform to accelerate the ability of our product teams to use LLM’s effectively and responsibly.
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Able to present complex technical concepts to business partners and senior management Experience leading teams with deep technical experience with enterprise development (e.g., Java, Python, SQL) and technology platforms (e.g., Databricks, Spark, Snowflake, Azure) Experience with establishing SDLC and Data Engineering best practices Experience with leading fine tuning, scaling, monitoring and alerting initiatives.
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Consult and implement data governance and management policies to support Dolby's position as trusted custodian in the media, entertainment, and technology ecosystems. We are interested in a variety of topics including large-scale distributed systems, stream processing, edge computing, applied machine learning and AI, big graphs, natural language processing, big data management, and heterogenous data analytics.
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Hands on Big Data / Distributed / cloud technology (AWS Big data services like lambda, glue, glue emr and Spark Architecture, Performance tuning ,Spark SQL, Streaming, KAFKA, Entitlements etc.
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Specifically this role will lead and contribute towards building aspects of centralized event driven data streaming platform and services focused on LSE(Large Scale Events) prevention and mitigation, data center operations, and hardware quality management for Oracle Cloud Infrastructure (OCI.
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Familiarity with using cloud services (AWS, Google, Azure) or Big Data tools (Hadoop, Hive, Spark) in data science solutions. Underpinned by technology, data, analytics, AI, change management, talent and sustainability capabilities, our Strategy & Consulting services help architect and accelerate all aspects of an organization’s total enterprise reinvention.
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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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You will apply modern software and data engineering stacks to enable training, deployment, and lifecycle management of a variety of ML models; supervised and unsupervised, deep learning and classical.
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Knowledge of Big Data platforms (e.g. Hadoop, Map/Reduce, Spark, HBase, HDInsight, Data Bricks, Hive) and with programming languages like UNIX shell scripting, Python etc. In addition, they will also assist in developing the data pipeline (using Informatica BDM or Python) to ensure ongoing data collection, consolidation, and management.
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We are interested in a variety of topics including data visualization and interactivity, applied machine learning and AI, AI bias and trust, big graphs, natural language processing, data mining, big data management, and heterogenous data analytics.
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Execute Master Data Management (MDM) and Governance initiatives to guarantee data precision and uniformity across all levels of the organization. Strong knowledge of Master Data Management (MDM) and experience with MDM implementation.
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The Corporate Treasury Quantitative Analytics team at Bank of America is staffed by analysts who apply an extensive set of quantitative methods for effective asset liability management. Experience in one or more of the following languages: Python, SQL, Spark.
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