The team is a multidisciplinary group of engineers and scientists engaged in a fast paced mission to deliver new products. Job summaryYou will be working with a unique and gifted team developing exciting products for consumers. You will join our talented team of engineers, product managers, designers, and analysts in bringing new streamer support products from idea to launch. Our ideal candidate is a full-stack data powerhouse who is experienced in driving product decisions throughout the product lifecycle using data and experimentation. We are looking for an experienced data scientist to help develop new revenue streams for our creators, and enhance our existing offerings. As a data scientist at Twitch, you will shape the way product performance is measured, identify questions that guide our product strategy, apply machine learning methods to uncover new insights, and scale our analytic methods and tools to support our growing business.The Commerce team at Twitch is dedicated to building products and tools that help streamers make a living doing what they love. Keep an eye on all things Twitch on Linkedin, Twitter and on our Blog.About the Role:Data scientists play a central role in Twitch's data-driven decision-making process. Twitch also hosts TwitchCon, where we bring everyone together to celebrate and grow their personal interests and passions. We bring the joy of co-op to everything, from casual gaming to esports to anime marathons, music, and art streams. Job summaryAbout Us:Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments. We show that our proposed approach works well both qualitatively and quantitatively. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: “Is this item similar to what you have in mind?” With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. However, this approach requires the user to possess an example representation of their desired item. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object).
#The seeker the who amazon how to
When searching online, users may not know how to accurately describe their product of choice in words.
#The seeker the who amazon series
This paper introduces Seeker, a system that allows users to adaptively refine search rankings in real time, through a series of feedbacks in the form of likes and dislikes.