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Our team develops, implements, and maintains recommendation and personalization algorithms for Disney Streaming’s suite of streaming video apps, notably Disney+ and Hulu. As a member of this team you will collaborate across Engineering, Product, and Data teams to apply machine learning methods to meet strategic product personalization goals, explore innovative, cutting edge techniques that can be applied to recommendations, and constantly seek ways to optimize operational processes.
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In supporting rule analysis, Teammate should be able to Independently perform sophisticated data analytics that fit to the fraud problem being observed (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data.
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Experience with AWS cloud services : EC2 , EMR, RDS, Redshift , Modern data platforms, Snowflake , dbt , Fivetran and Airflow. 3+ years of experience with Cloud based DW such as Redshift , Snowflake etc.
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6+ years of experience using eDiscovery collection and processing software (Relativity, Brainspace, Google Vault, etc.) Experience with data collection and investigation in Cloud technologies.
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The Generative AI Architect, you will apply expertise in data science, specifically in artificial intelligence, machine learning, and software architecture to address sophisticated business challenges.
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Experience in Natural Language Processing, Machine Learning Ops and data pipelines. Exposure to and experience building Machine Learning applications and services with cloud scalability.
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Experience working with cloud based data warehouses like Google BigQuery, Redshift, or Snowflake. Bachelor’s or Graduate degree, Field of Studies required: In a quantitative field or commensurate experience such as Statistics, Business Analytics, Data Science, Applied Math, Econometrics or Operations Research.
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Accredited Artificial Intelligence (AI) / Machine Learning (ML) certifications (e.g., AWS, Google Data Analytics, Microsoft Azure) Experience applying data science techniques, including time series, supervised learning (e.g., regression analysis, classification analysis, univariate models, multi-variate models), and/or unsupervised learning (e.g., clustering, association, dimensionality reduction), to Finance use cases such as forecasting and predictive analytics.
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The People Analytics team is globally respected as a leader in its field and is dedicated to staying on the cutting edge of business intelligence and data science methodology and technology.
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Tracking and Reporting Create and analyze monthly client reports and provide competitive insights Pull relevant program data from popular analytics platforms such as Google Analytics, Looker/Google Data Studio, etc Qualifications: Minimum of 2+ years of experience in SEO with a proven track record required Excellent keyword research skills required Excellent writing, editing, and proofreading skills.
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We want Data Science/Machine learning/Data Analyst and Java Full stack candidates. For data Science/Data Analyst/AI/Machine learning Positions.
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Our work includes novel areas including machine learning/ AI, data visualization, and cloud technology. We are hiring a Senior Statistical Analyst to leverage cutting-edge data science tools and methodologies on real-world clinical data for scientific research in precision medicine.
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5+ years of experience in data analysis or data science, with 3+ years focusing on machine learning problems, ideally in a relevant space (KYC, sanctions detection, anti-fraud detection, treasury management, crypto/blockchain data science.
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Broad hands-on experience in AWS Cloud serverless components and ability to build and maintain cloud data processing systems (i.e. ECS, EKS, S3, Lambda, SQS/SNS, PostgreSQL, Dynamo, Sage maker, Athena, etc.
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Must required Core Java, Typescript, Microservices, Rest API, scripting, AWS (specially lambda, code pipeline, Api gateway, cloud watch, Dynamodb, AWS Cli ), Docker, Kubernetes Good to have Quarkus, GRAAL VM, Java 17, Mockito, Openshift (PAAS), podman.
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