Our team is passionate about Brands who sell on Amazon - we help them grow their businesses, build their story, and serve their customers. How do we do this? Data! Help us serve this valuable data to our Brands in digestible ways so they can run their businesses more effectively.
Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.
Please visit https://www.amazon.science for more information.
We are looking for a customer obsessed Applied Scientist who can apply the latest research, state of the art algorithms and machine learning to build highly scalable systems in the e-commerce domain. As a member of our team you will develop and evaluate machine learning models using large data-sets and cloud services to drive the growth of Brand owners on Amazon. Working closely with best-in-class engineers you will have the opportunity to research and implement novel ML and statistical approaches, apply a variety of machine learning algorithms, including deep learning, and work on one of the world's largest data sets to influence the long term evolution of our science roadmap.
· Ph.D./M.S. in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field.
· 6+ years of hands-on experience in predictive modeling, analysis, and Machine learning.
· 3+ years hands-on experience in Python, Scala, Java, C#, C++ or other similar languages
· Proficiency in model development, model validation and model implementation for large-scale applications
· Ability to convey mathematical results to non-science stakeholders
· Strength in clarifying and formalizing complex problems
· A PhD in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, or equivalent).
· 10+ years of practical experience applying ML to solve complex problems in an applied environment.
· Significant peer-reviewed scientific contributions in premier journals and conferences.
· Strong CS fundamentals in data structures, problem solving, algorithm design and complexity analysis.
· Skilled with Java, C++, or other programming language, as well as with R, SAS, MATLAB, Python or similar scripting language.
· Extensive knowledge and practical experience in several of the following areas: machine learning, statistics, NLP, deep learning, recommendation systems, dialogue systems, information retrieval
· Experience working with Deep Learning frameworks (MxNet, TensorFlow, etc.).
· Proven track record in technically leading and mentoring scientists.
· Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
· Ability to manage and quantify improvement in customer experience or value for the business resulting from research outcomes.