Ranking Science & Research Leader
- Los Gatos, California
- Data Science and Engineering
Netflix is seeking an interdisciplinary machine learning leader to help us imagine the next version of our globally-deployed recommendation algorithms. Is that you?
Some examples of the problems you might tackle in your new role:
• How do we leverage our data to choose a small, relevant, and diverse subset of titles from our extensive catalog to present to each user? And how do we do this in half a second or less each time…. billions of times a day?
• How do we improve search so that it surfaces the most relevant titles based on a query the user enters and also perhaps based on the user's viewing history itself as well?
• How do use the above systems to also inform our catalog and decide which new content to purchase for our members and how it will fare once available on the service?
Netflix is a fact-based, analysis-driven organization: The improved algorithms you lead will be A/B tested quickly and rigorously and you will have direct, measurable impact to the bottom-line. Remember: At Netflix, everything is a recommendation!
As Ranking Science & Research Leader, you will…
• lead a world-class team of machine learning researchers with an extensive track record in industry and academia.
• bring a combination of rigor and innovative algorithm design to extract relevant insights from -global-scale data to meaningfully improve user experience.
learn, develop, and apply new techniques in the intersection of math, probability, and optimization.
• translate unstructured, complex business problems into an abstract mathematical framework, making intelligent approximations when needed to put your algorithm to work at scale.
• work closely with various product development engineering teams to solve key personalization, discovery and search problems.
• collaborate with and present progress to the company's leadership.
• Leadership experience in machine learning, personalization, recommendation, search and consumer data. Industry experience preferred, publications are a plus
• PhD degree in Computer Science, Statistics, Operations Research, Mathematics, or related field.
• A background in machine learning and related sub-areas including ranking, personalization, search, recommendation, explore/exploit, causal learning, reinforcement learning, deep learning and probabilistic modeling.
• Significant experience in research and industry.
• A track record of leveraging large amounts of data to solve real-world problems.
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