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Netflix: Senior Data Scientist, Streaming Science & Algorithms

Join Streaming Science and Algorithms team, find ways to improve the streaming Quality of Experience using algorithms applied to big data.

Netflix Company: Netflix
Location: Los Gatos, CA

Apply online

Netflix is revolutionizing entertainment. We deliver over a billion hours of streaming per month of movies and TV shows across more than 40 countries. Our data-driven culture allows us to continuously innovate and provide the best experience for our members. We are at the forefront of using advanced mathematical models and algorithms on big data to personalize the experience that every member has, and our scientists and engineers are constantly looking for new ways to improve the state of the art. Netflix data science is a unique combination of big data, algorithms, and entertainment: where else do you get to work on big data and win three Emmys and a Golden Globe Award!

The Science and Algorithms team at Netflix is a talented group of researchers from various disciplines, who work on interesting problems from different parts of the company. This position is in the Streaming Science and Algorithms team that is actively working on ways to improve the streaming Quality of Experience (QoE) for our members using algorithms applied to big data. Problems we tackle are diverse and range from optimizing the streaming algorithms to working with the digital supply chain that goes all the way to the studios in Hollywood! The streaming QoE space is still nascent and as part of our team, you will have the opportunity to do groundbreaking applied research that will shape the industry.

Check out this tech blog post to learn more:

We are in need of a creative thinker with deep expertise in designing scalable algorithms using mathematical techniques. You will work with amazing colleagues, brainstorm new ideas, and develop algorithms to solve challenging problems that have a substantial impact on the product, business, and millions of our members around the globe.

  • PhD or MS in Computer Science, Machine Learning, Operations Research, Engineering, Statistics, Mathematics or related field.
  • 4+ years relevant experience with a proven track record of leveraging analytics and large amounts of data to drive significant business impact.
  • Expertise in machine learning, predictive analytics, statistical modeling, and/or data mining algorithms. Must have knowledge/experience in some or all of the following: Multivariate Regression, Logistic Regression, Support Vector Machines, Bagging, Boosting, Decision Trees, Lifetime Analysis, Clustering, Mathematical Optimization, and Stochastic Processes.
  • Strong algorithmic thinking and independent research ability.
  • Passion for learning and innovating new methodologies at the intersection of applied math/statistics/computer science.
  • Proficient at translating unstructured business problems into an abstract mathematical framework.
  • Ability to make intelligent approximations of mathematical models in order to make them practical and scalable.
  • Exceptional interpersonal and communication skills, including the ability to describe the logic and implications of a complex model to all types of business partners.
  • High-energy self-starter with a passion for your work, attention to detail, and a positive attitude.

Technical Skills
  • Proficiency in at least one statistical analysis tool such as R, SAS, and/or Weka.
  • Above average capabilities with SQL.
  • Experience with distributed databases and query languages like Hive/Pig and/or general map reduce computing is a plus.
  • Knowledge of common data structures and ability to write efficient code in at least one language is a plus (preferably Java, Python, or Perl).

You will have the opportunity to impact the business in a meaningful way. You will have the freedom to innovate, solve interesting problems and influence in a fast paced, exciting environment. You will work with smart people who love to solve hard problems, and who not only expect, but also foster high performance.

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