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1 week ago(10.8.2018 20:26)
Amazon Development Center Germany GmbH
As an Applied Scientist, you will transform the way people enjoy music through personalized recommendations, search results or songs sequencing. You will collaborate with scientists and engineers to design and evaluate new machine learning models using audio features, meta-data, search queries or customer's listening behavior to create a personalized customer experience.
Amazon Music is looking for an Applied Scientist in Berlin with strong software development skills as we build a team of talented and passionated applied scientists to personalized the customer experience of Amazon Music. Our mission is to push the envelope in music recommendation and personalization. As an Applied Scientist, you will participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning. You will work with software developers and other teams to design and implement statistical models for recommending the right music at the right time considering context and behavior of a customer. Imagine being a part of an agile team where your ideas have the potential to reach millions. Picture working on cutting-edge consumer-facing products, where every single team member is a critical voice in the decision-making process. Envision being able to leverage the resources of a Fortune-500 company within the atmosphere of a start-up.
Graduate degree (MS or PhD) in Computer Sciences, Mathematics, or Statistics with specialization in machine learning.
2+ years of hands-on experience in predictive modeling and analysis, and in deploying machine learning models in production.
3+ years of hands-on experience in programming languages such as Java and Python
PhD with specialization in machine learning with at least 5 years of related work experience.
Strong software development skills.
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.
Entrepreneurial spirit combined with strong problem solving skills.
Previous experience with methods in the Reinforcement Learning space (for example, Multi-Armed Bandits).
Strong publication record in top tier ML conferences (NIPS, ICML, AISTATS, etc.).