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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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7+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience. Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop and implement in production algorithms for personalization, recommendation, and other predictive systems; maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams.
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In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings. Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate.
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Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience. Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark.
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Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets.
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You will be expected to lead recommendation and personalization algorithm research, development, implementation, and optimization for product areas, and to coordinate requirements and manage stakeholder expectations with Product, Engineering, and Editorial teams.
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5+ years of experience developing algorithms for deployment to production systems. AWS, Docker, Airflow, Databricks. Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day - from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.
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Production experience with developing content recommendation algorithms at scale and familiar with metadata management, data lineage, and principles of data governance. Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
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As a Senior Associate in our Risk Analytics and Modeling team, you’ll have the opportunity to apply your quantitative, modeling and analytical skills in a real world environment to develop or validate statistical, financial engineering, and AI/machine learning models in the areas of credit risk, market risk, assets and liabilities management, fraud detection, anti money laundering and other functional modeling and analytics area.
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We are a multidisciplinary algorithmic trading team with expertise in machine learning, numerical optimization, software engineering, distributed systems, electricity markets, and trading.
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Hands-on experience on design, and optimizing LLM, natural language processing (NLP) systems, frameworks, and tools. Experience in Azure cloud technologies like PySpark, Synapse, ADF, Databricks, Python, Scala and SQL.
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Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, monitoring, and feedback loop. Python ecosystem preferred, R will be acceptable, machine learning libraries & frameworks (e.g. TensorFlow, PyTorch, scikit-learn) and familiar with data processing and visualization tools (e.g., SQL, Tableau, Power BI.
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The Principal Architect, Machine Learning Solutions (ML), is responsible for developing innovative solutions and technology strategies specifically focused on machine learning (ML) systems.
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Bachelor’s, Master’s degree, or Ph. D. in a relevant field: Statistics, Applied Mathematics, Operations Research, Computational Physics, Computer Science, Engineering, etc. We are looking to hire a permanent placement role for a Mid-Level Machine Learning/Data Scientist Engineer to join our hybrid company in Houston, Tx. Candidates should be interested in building intelligent space systems to modernize satellites' operations.
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Individuals in this role are expected to be recognized experts in areas such as artificial intelligence, machine learning, computational statistics, and applied mathematics, particularly including areas such as deep learning, graphical models, reinforcement learning, computer perception, natural language processing and data representation.
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