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Sr. Applied Scientist, Prime Video Demand Insights
Sr. Applied Scientist, Prime Video Demand Insights-February 2024
Seattle
Feb 10, 2026
About Sr. Applied Scientist, Prime Video Demand Insights

  Would you like to shape the future of the video entertainment industry for movies, TV and live sports events? Does solving complex modeling problems within large scale distributed systems excite you? If you answered yes, we have an opportunity for you!Prime Video is building the future of TV for customers in over 200 countries to enjoy an endless selection of movies, shows and sports on their TV, mobile devices and desktop. We deliver high-quality video to Amazon customers through subscriptions (Amazon Prime, and Channels) as well as purchases, rentals, and free with ads.In Prime Video Demand Insights, our mission is to automate infrastructure scaling. We are growing a team specialized in time series modeling and forecasting math. This team will invent and develop algorithms for forecasting multi-dimensional related time series. The team will develop forecasts on key business dimensions with optimization recommendations related to performance and efficiency opportunities across our global software environment.As a founding member of the core team, you will apply your deep coding, modeling and statistical knowledge to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on retrieving, cleansing and preparing large scale datasets, training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with complete independence and are often assigned to focus on areas where the business and/or architectural strategy has not yet been defined. You must be equally comfortable digging in to business requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions.You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than delivering for our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies to your solutions. If you crave a sense of ownership, this is the place to be.We are open to hiring candidates to work out of one of the following locations:Seattle, WA, USA- 3+ years of building machine learning models for business application experience- PhD, or Master's degree and 6+ years of applied research experience- Knowledge of programming languages such as C/C++, Python, Java or Perl- Experience programming in Java, C++, Python or related language- Experience with neural deep learning methods and machine learning- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.- Experience with large scale distributed systems such as Hadoop, Spark etc.Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on t

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