The Amazon Search team creates powerful, customer-focused search and advertising solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, A9 Product Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. Our Search team works to maximize the quality and trustworthiness of the search experience for visitors to Amazon websites worldwide.
Our mission is to provide customers' trust and confidence in Amazon Search shopping experience. We identify problems that are customer trust busters at Amazon, deliver scalable and responsive solutions to these issues, and build experiences that gain customer trust using advanced machine learning methods. We carefully monitor the trustworthiness of the search results and dive deep when we see an unusual pattern. Most of the models used by our team is semi-supervised or unsupervised using small amount of labeled data.
In this role you will leverage your strong statistical background to help build the next generation of our machine learning methods to discover untrustworthy search engagements, unsual patterns, and estimate a probability of risk for each item. This role requires a pragmatic technical leader comfortable with ambiguity, capable of summarizing complex data and models through clear visual and written explanations. The ideal candidate will have experience with machine learning models, graph algorithms, and information retrieval algorithms at scale. Additionally, we are seeking candidates with strong rigor in applied sciences and engineering, creativity, curiosity, and great judgment.
· Masters degree in Statistics, Mathematics or related field, or equivalent experience.
· A strong ability in statistical modeling and analysis to provide rigorous solutions that address business needs.
· Excellent data visualization and written communication skills to translate complex models and analysis results into layman terms.
· Hands on development experience in C++, Java, or other OOP language.
· Experiencing with programming in R, MATLAB, Python or similar scripting languages.
· Experience with data visualization technologies, including interactive visualization (i.e. D3js, Bokeh, Matplotlib, FusionCharts, or HighCharts).
· Knowledge of Information Retrieval theory and practice.
· PhD in Computer Science, Statistics, Mathematics or related field, or equivalent professional experience.
· 2+ years of hands-on experience with data analysis for Search, Recommendation, or Advertising space.
· Strong working knowledge of web-scale data processing (e.g., Hadoop, Pig, Spark, Cosmos).
· Strong background in machine learning and data mining.
· Experience mentoring/training junior scientists on complex scientific issues.
· Strong verbal and written communications skills; experience presenting complex technical information, succinctly, to technical and business audiences