Prime Video (PV) is building the future of TV. Today, PV customers in over 200 countries enjoy an endless selection of movies, shows and sports on their TV, mobile devices and desktop. PV Personalization want to make it very simple for customers to navigate the vast selection of movies and shows to find something to watch on every visit. The problem is nuanced: millions of customers stream billions of minutes of content on the service, the catalog is large and diverse with a mix of Original Prime Video content, Live sports like football, tennis and soccer, the latest movies to rent or buy, or a-la-carte channel subscriptions like HBO. The end goal is to build a Prime Video homepage and marketing outreach system that feels personal despite the variety offered on the service, and grow long term customer engagement. The success of our efforts directly impacts customer's ability to find something to watch during a visit to Prime Video, and through it, directly impacts Prime Video's key business metrics on customer engagement.
We consider our position as a once-in-a-lifetime opportunity to shape the future of TV for billions of viewers worldwide. We're looking for a thought leader in machine learning science to lead the charge with cutting edge research to address the unique and nuanced problems in our space, especially the most ambiguous ones with long term 2-5 year success timeframes. You will work with a number of senior and junior scientists on the team as well as have the opportunity to grow the science team by more than double as we greatly expand investment to keep pace with the size of the customer and business opportunity.
Technologically, the core of the efforts revolve around gathering and processing the vast variety of customer behavior data available to Amazon(think years of video watching behavior, customer purchases, IMDb data on movies/shows), researching and building machine learning algorithms, and deploying services wrapping ML applications that power an interactive content discovery experience at scale. Every year, we run hundreds of A/B experiments to test our hypotheses. We know our future success is inextricably tied to being a center of excellence in machine learning science and we invest in itwe are early adopters of cutting-edge tech such as deep learning and publish papers both internally within Amazon and externally to conferences. This role offers applied machine learning at its bestcontemporary research problems based on rich Amazon datasets, massive customer experience impact in shaping the future of online TV, and clear impact to business KPIs.
• PhD in Computer Science (Machine Learning, AI, Statistics, or equivalent);
• 7+ years of experience applying ML to solve complex problems for large-scale applications
• Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives
• Ability to manage and quantify improvement in customer experience or value for the business resulting from research outcomes
• Strong fundamentals in problem solving
• Ability to work with multi-disciplinary teams across science and technology fields.
• Superior verbal and written communication and presentation skills, ability to convey rigorous science concepts and considerations to non-experts.
• Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, recommendation systems, deep learning, information retrieval.
• Track record of scientific publications in premier journals and conferences.
• Skilled with Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language.
• Professional experience in software development (software design and development life cycle).
• Ability to distill informal customer requirements into problem definitions, dealing with ambiguity and competing objectives.
• Ability to manage and quantify improvement in customer experience or value for the business resulting from research outcomes.
• Project management experience for working on cross-functional projects.
• Proven achievements of developing and managing a long-term research vision and portfolio of research initiatives, with algorithms and models that have been successfully integrated in production systems.