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Our mission and team expertise spans beyond software to advanced sensor systems, algorithms, embedded systems, signal processing, and machine learning. Spun out of Stanford in 2019, we have built an intelligent analytics platform fueled by our revolutionary device which uses machine perception and sensor fusion to provide an operations digital twin that combines factory operator activity with machine data for the first time.
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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. 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 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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MS or PhD in statistics, math, computer science, or related quantitative field. 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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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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Proven publications in machine learning, AI, computer science, statistics, applied mathematics, data science, or related technical fields. 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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Relevant data science skills TensorFlow Keras PyTorch Python Numpy Panda What You Will Be Doing Tech Breakdown · 90% Machine Learning / Data Science · 10% Other data Daily Responsibilities · 80% Hands On · 20% Team Collaboration The Offer Stock Options available You will receive the following benefits: One of the most interesting aspects of working at a startup company is gaining equity in the company, which means our success is your success.
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The Data Science team at Meijer leads the strategy, development and integration of Machine Learning and Artificial Intelligence at Meijer. Build production ready prototypes for, and iteratively develop, end-to-end data science pipelines including custom algorithms, statistical models, machine learning and artificial intelligence functions to meet end user needs.
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FM Global is seeking a Machine Learning Operations Data Engineer II to join our AI/ML team to support Machine Learning Engineering, working very closely with Data Science, Data Engineering, Subject Matter Experts and Solution Architecture teams.
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As a Machine Learning Operations Data Engineer II you will develop platform tooling, deploy data science models to production and monitor production performance.
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Engineer will work closely with Business domain experts and Data Scientists to solve real-world oil and gas midstream problems using advanced analytics, machine learning, and artificial intelligence.
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Building streaming data pipelines using Kafka, Spark, or Flink. 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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sensor engineer machine learning computer science data jobs Company: University Of California
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