Netflix is seeking a recent (or soon-to-be) PhD grad who is outgoing and curious, to work as a data miner, statistical modeler and algorithm designer. In this role you will have the opportunity to work closely with product managers and engineering teams to solve key content merchandizing problems relating to personalization, discovery and search. You will apply the mathematical rigor from your academic training and learn to make intelligent modeling approximations to create recipes that extract relevant insights from billions or rows of "real-world" data to meaningfully improve user experience. Here are some examples of the problems we tackle:
At Netflix, our culture and management is committed to being a fact-based and analysis driven organization. Because of this, your contributions in solving problems like these will be A/B tested quickly and rigorously and you will have direct and measurable impact to the bottom-line. You will also get a chance to work with other world-class data scientists that you can learn from and hone your skills.To succeed in this role you should
- How do we choose a small but relevant and diverse subset of titles from our extensive catalog to present to each user leveraging all our data? And how do we do this in half a second or less each time…. billions of times a day?
- Searching on game consoles and other devices using a software keyboard is not easy… So how do we delight the user by recognizing what they are searching for with the fewest possible inputs whether it is an actor/director/title/genre/award/film festival search and showing them relevant results from our catalog.
- An original title exclusive to Netflix is about to be launched. How do we decide from just unstructured text descriptions of the content (say a book that the title is based on) to which users the title should be promoted?
We’re proud of our expertise we’re developing in personalization and ultimately translating this into consumer value – so come be a part of the story!
- Have an expert grasp of commonly used data mining / statistical learning techniques… including the corresponding mathematical foundations such as probability/linear algebra/optimization
- Be able to grasp the problem at hand and recognize an appropriate approach/model to solve it
- Be comfortable with commonly used computing and database environments and be familiar with some tools for manipulating data and fitting models
- PhD. degree in Statistics, Mathematics, Operations Research, CS (Data mining), Econometrics or equivalent/related degree.
- Expertise in statistical modeling/data mining algorithms . Must have knowledge/experience in some/all of the following: Multivariate Regression, Logistic Regression, Support Vector Machines, Estimation, Bagging, Boosting, Decision Trees, common clustering algorithms.
- Experience in Optimization, Stochastic Processes or numerical computing a plus.
- Experience working on machine learned ranking problems a plus.
- Above average interpersonal, communication and presentation skills – must be able to explain technical concepts and analysis implications clearly to a wide and non-technical audience.
- Proficiency in at least one data analysis tool such as R, Splus or Matlab.
- Knowledge of common data structures and ability to write efficient code in at least one scripting language such as Python or Perl
- Experience with Java/C++ is a plus
- Experience with SQL databases or distributed databases and query languages like Hive/Pig/Sawzall and/or general map reduce computing is a plus
Netflix, Inc. is an online movie rental subscription service in the United States, providing approximately 7.5 million subscribers access to...