Do you want to lead a team that builds new technology and science used by millions of people?
The Devices Engagement Personalization Science team is looking for an Applied Science leader who has a solid background in applied Machine Learning, deep passion for building data-driven products, ability to communicate data insights and scientific vision, and has a proven track record of leading both applied and data scientists to execute complex projects and deliver high business impact.
Personalized recommendations are a key feature for all Amazon tablets and we make it easier for customers to find the content that interests them. Examples include:
· Our onboarding videos and content suggestions are personalized for each customer.
· We suggest a set of videos, books, and apps for our users that are tailored for their specific interests.
· Our special offers and marketing campaigns are personalized.
In this role, you will:
· Lead a team of talented applied scientists and data scientists to deliver production level, customer facing ML solutions that empower our software to make the best set of suggestions.
· Work closely with product and technology leaders across Amazon.
· Develop a science roadmap and collaborate with stakeholders.
· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.
· Hire and develop top talent and provide technical/career development guidance to both scientists and engineers in the organization.
Amazon is an Equal Opportunity Employer – Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation / Age
· Graduate degree in Computer Science, Operations Research, Mathematics or related fields with a focus on machine learning algorithms
· At least 5 years of experience managing a team of applied scientists
· At least 5 years of experience in building large scale machine learning solutions at scale
· Excellent oral and written communication skills and a willingness to communicate complex solutions and results to all levels of the organization
· Experience using Java, C++, or other programming language, as well as with R, MATLAB, Python or similar scripting language
· Ph.D. with concentration in Natural Language Processing, Machine Learning, Statistics or another highly quantitative field.
· Experience in building large-scale machine learning models and infrastructure for online recommendation, ads ranking, personalization, or search, etc.
· Published research work in academic conferences or industry circles
· Experience in recommender systems and neural networks