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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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We are expanding in data science to provide the best information possible utilizing the latest techniques in Machine Learning (including Deep Learning, Neural network.
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Our team develops 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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5+ years of relevant experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience. In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings.
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Algorithm Development and Maintenance: Utilize cutting edge machine learning methods to develop algorithms for personalization, recommendation, and other predictive systems; deploy and maintain algorithms deployed to production and be the point person in explaining methodologies to technical and non-technical teams.
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Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate. 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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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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Building streaming data pipelines using Kafka, Spark, or Flink. MS or PhD in statistics, math, computer science, or related quantitative field. Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.
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Collaborate with product and business stakeholders: Identify and define new personalization opportunities and work with other data teams to improve how we do data collection, experimentation and analysis.
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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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As a member of the Clinical Data Science Leadership Team, the Executive Director, Data Science is accountable for Development’s machine learning and artificial intelligence strategy, overseeing the expansion of the industry-leading Advanced Analytics organization to address the growing opportunity for value generation through the application of AI/ML, including large language models (LLMs), across Development.
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Conduct ongoing research to stay abreast of the latest developments in machine learning, deep learning, and data science, and apply this knowledge to enhance project outcomes.
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Demonstrated expertise in time-series analysis, structural equation modeling, deep learning and predictive analytics, simulation and data visualization. The mission of the Center for Biostatistics and Health Data Science (CBHDS) is to achieve excellence in Virginia Tech’s health- and medically-related research portfolio through fostering collaborations across biostatistics, data science, health analytics, computer science, engineering, bioinformatics, biology, database management and integration, project coordination, clinical practice, health economics, translation and public policy.
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8+ years of experience in developing business applications for Machine Learning and Data Science workloads. You will work closely with data scientists, machine learning engineers, and other software engineers, using the latest tools and technologies to develop analytic solutions and integrate analytic models with existing business applications.
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machine learning data science jobs Company: Jpmorgan Chase in Wailuku, Hawaii
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