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Senior Research Scientist - Discovery (Machine Learning)

  • Los Gatos, California
  • Science and Analytics
Netflix is seeking an outgoing, curious, interdisciplinary machine learning expert to help us imagine the next version of our globally-deployed recommendation algorithms. The improved algorithms you develop will be A/B tested quickly and rigorously and you will have direct, measurable impact to the bottom-line. Is that you?
 
To learn more about our research and analytics work, you can visit our research page here.

As a senior research scientist, you will:

  • Join a world-class team of machine learning researchers with an extensive track record in both academia and industry.
  • Bring a combination of mathematical rigor and innovative algorithm design to create recipes that extract relevant insights from billions of rows of 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.        
  • Interact with and report to an audience that includes Directors, Vice-Presidents and the Chief Product Officer.

Some examples of the problems you might tackle in your new role:

  • How do we anticipate how our titles will do in the future and how our recommendation algorithm will influence their trajectory?
  • 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 promote our new original movies to new users in different countries and also dynamically adapt our recommendations online based on realtime user feedback?

Qualifications:

  • PhD degree in Computer Science, Statistics, Operations Research, Mathematics or related field.
  • Strong background in machine learning using unsupervised and supervised methods.        
  • 5+ years of research experience.       
  • Proven track record of leveraging large amounts of data to solve real-world problems.
  • Experience with contextual bandits, computational advertising, online learning, reinforcement learning, causal learning, generative-adversarial networks and deep learning would be a plus.