2 years ago

https://netflixtechblog.medium.com/?source=post_page-----394bc7d0f94c-----------------------------------https://www.linkedin.com/in/martintingley/ with https://www.linkedin.com/in/wenjing-zheng/, https://www.linkedin.com/in/simon-ejdemyr-22b920123/, https://www.linkedin.com/in/stephanielane1/, https://www.linkedin.com/in/mcfrl/, https://www.linkedin.com/in/tendulkar/, and https://www.linkedin.com/in/traviscb1998/ This is the last post in an overview series on experimentation at Netflix.

As a company, Netflix is organized to emphasize the importance of learning from data, including from A/B tests.

Although they are not informed captains on product decisions, data scientists, as interpreters of the data, are active contributors to key product decisions.

Because Netflix tests all ideas, and because most ideas are not winners, our culture of experimentation democratizes ideation.