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    Machine Learning Scientist - Los Angeles, United States - Netflix

    Netflix
    Netflix Los Angeles, United States

    Found in: beBee S2 US - 2 weeks ago

    Netflix background
    Full time
    Description

    Netflix is one of the world's leading entertainment services with over 260 million paid memberships in over 190 countries enjoying TV series, films and games across a wide variety of genres and languages. Members can play, pause and resume watching as much as they want, anytime, anywhere, and can change their plans at any time.

    The Content Data Science and Engineering (DSE) team is passionate about storytelling and bringing our members joy. Our mission to entertain the world is anchored in our content, and data is a crucial component in shaping our comprehensive content strategy. Using rich multi-modal data (text, video, audio, and streaming data), we leverage a variety of state-of-the-art ML tools to unlock unique insights that have high cross-company visibility and impact. Our team creates ML-based products focused on understanding and systematizing diverse member experiences and perspectives, that support our content partners in their complex and nuanced decision-making processes. We are a highly collaborative team that partners across Netflix to drive impact.

    We are seeking a Machine Learning Scientist to join the team. Your focus will be on developing foundational tools to support member analytics and understanding, including building models, exploring and charting their analytic implications, and developing and supporting high-impact ETLs and downstream tooling. This role features ample opportunity for project ownership and will support impactful decision-making. In success, you will collaborate on existing priorities but also will propose and execute on new opportunities.

    In this role, you will:

  • Design and implement state-of-the-art machine learning models for visual, audio, and textual data to enhance content understanding and improve content planning, content production, and promotion processes.
  • Collaborate with cross-functional teams, including product managers, engineers, data scientists, consumer researchers, and content teams, to define problem statements, define roadmaps, and execute on priorities.
  • Communicate complex concepts and the results of analyses with both technical and non-technical stakeholders to influence strategic decisions.
  • Engage with the ML community, internal and external, to learn, to teach, to contribute to building a great Netflix brand in ML.
  • Stay abreast of the latest developments in the field by attending conferences, reading research papers, and implementing promising novel ideas that can impact content workflows.

    About you

  • You have a strong foundation in machine learning and deep learning, including embedding methods, supervised and unsupervised learning, and deep learning architectures.
  • Advanced degree (MS or Ph.D.) in Computer Science, Cognitive Science, Electrical Engineering, or a related technical field with a focus on machine learning, artificial intelligence, or computer vision.
  • Strong programming skills in R/Python and experience with appropriate ML, AI, Mathematical Psychology, or Deep Learning modeling frameworks.
  • Experience with SQL, or with building end-to-end data pipelines.
  • You have a strong track record of solving complex problems with innovative solutions. You are able to both develop novel algorithms and adapt existing methods from the literature to new challenges.
  • You are an excellent communicator, capable of explaining complex technical details to both technical and non-technical audiences.
  • You are a self-starter who thrives under a high level of autonomy in a fast-paced environment and is enthusiastic about Netflix's unique culture.

    Netflix focuses on Digital Entertainment. Their company has offices in Los Gatos. They have a very large team that's 5000+ employees. To date, Netflix has raised $3.102B of funding; their latest round was closed on April 2018.
    You can view their website at or find them on LinkedIn.
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