Senior Data Scientist - Machine Learning Research
- 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. Is that you?
To learn more about our research and analytics work, you can visit our research page here.
As a senior data 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 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?
Netflix is a fact-based, analysis-driven organization:
- The improved algorithms you develop 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!
- 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 and/or deep learning would be a plus.