Applied Scientist, Amazon Music Machine Learning/personalization

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Imagine shaping how millions of customers discover and engage with digital music and audio, leveraging the resources of a Fortune-500 company within the atmosphere of a start-up. Love collaborating with a wide range of internal and external partners to drive successful partnerships? If so, come and build the future of music and audio with us. Transform how people enjoy music, audiobooks & podcasts with personalized recommendations as our Applied Scientist in Berlin. Lead the design, development & deployment of data-driven models that recommend the right audio at the right time based on customer context & behavior. Leverage audio data, metadata & listening patterns to create innovative features that personalize the Amazon Music experience. Collaborate with engineers and business teams to ensure coherent personalization across Amazon Music, from conception to productionization. You will make listening magical again through the power of personalization Key job responsibilities
- Create Innovative ML Solutions for Customized Musical Joy You will architect novel machine learning solutions that delight customers with personalized music experiences tailored just for them. Through A/B testing and online experiments done hand-in-hand with engineering teams, you'll implement and validate your ideas and solutions.
- Chart the Course to Musical Discovery As a leader in Amazon Music's personalization mission, you'll set the strategy for your team's personalization science and recommender systems. You'll scout out new directions to guide Amazon Music's quest to match each customer with their perfect playlists. *Publish Pioneering Research You'll produce innovative research on recommender systems that shapes the field and meets the high standards of peer-reviewed publications. You'll cement your team's reputation as thought leaders pioneering new recommenders. A day in the life Lead innovation in ML to shape Amazon Music experiences for millions. Collaborate with talented engineers and scientists to guide research and build scalable models across our audio portfolio - music, podcasts, live streaming, and more. Drive experiments and rapid prototyping, leveraging Amazon's data at scale. Innovate daily alongside world-class teams to delight customers worldwide through personalization. About the team Bring music to life through machine learning Our Berlin teams breathe passion into Amazon Music, optimizing each recommendation for our customers' unique tastes. Scale your work across our music apps, web player and Alexa devices, fine-tuning the algorithms behind our popular recommendation and personalization features. Collaborate with leading ML experts in Seattle and San Francisco to create the future of musical engagement, one stream at a time. Help us surprise and delight tens of millions of Amazon Music fans with an ever-better listening experience. Join our mission to personalize music through machine learning
- Experience working effectively with science, data processing, and software engineering teams.
- Proven track record of innovation in creating novel algorithms and advancing the state of the art.
- Strong publication record in top tier ML conferences (NIPS, ICML, AISTATS, etc.).
- Experience in professional software development.

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