What would you do if you had access to the world’s largest product catalog with billions of products, offers, images, reviews, searches, and much more? Amazon Selection and Catalog Systems is looking for an innovative and customer-focused senior applied scientist to lead the science strategy to improve the data quality of the world’s biggest product catalog, utilizing state-of-the-art machine learning techniques.
An information-rich and accurate product catalog is a critical strategic asset for Amazon. It powers unrivaled product discovery, informs customers’ buying decisions, offers a large selection and positions Amazon as the first stop for our customers. Maintaining and improving the accuracy of product catalog is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality).
You will conceive innovative solutions to measure and improve the quality of various aspects of our product catalog and influence the way millions of our customers discover and buy our products worldwide. The opportunity (puzzle to solve) is that there is no single solution as the problem scope is varied and diverse. The solutions you build will vary from simple rule based systems to machine learning, semantic analysis and text processing. You will have the opportunity to design new data analytical workflows at a scale rarely available elsewhere, utilizing state-of-the-art data science and machine learning tools such as Spark, Python, and Theano and Amazon’s cloud computing technologies such as Elastic Map Reduce (EMR), Kinesis, and Redshift. You will apply your knowledge in data science by creating algorithmic solutions that combine techniques such as clustering, pattern mining, predictive modeling, deep learning, statistical testing, information retrieval, and natural language processing and apply them to the voluminous data describing the products in the catalog and the customer interactions. You will evaluate with scientific rigor and provide inputs to business strategy and technical direction. You will collaborate with software engineering teams to integrate your algorithmic solutions into large-scale highly complex Amazon production systems.
Please visit https://www.amazon.science for more information.
Key job responsibilities
· Lead science strategy for product catalog data quality improvements.
· Map business requirements and customer needs to a scientific problem.
· Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization.
· Research, design, implement and deploy scalable machine learned models, including the application of state-of-art deep learning, to solve problems that matter to our customers in an iterative fashion.
· Mentor and develop junior applied scientists and developers who work on data science problems in the same organization.
· Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
· PhD degree with 4+ years of applied research experience or a Master's degree and 6+ years of experience of applied research experience
· 3+ years of experience in building machine learning models for business application
· Experience programming in Java, C++, Python or related language
· 6 +/- years of experience using mainstream programming language (C++, Java, Python, or similar).
· 6 +/- years of experience using object oriented programming, data structures and algorithms, and software system design.
· 6 +/- years of experience using a broad set of supervised and unsupervised ML approaches and techniques ranging from Regression to Deep Neural Networks.
· Proven track record of successfully applying ML-based solutions to complex problems in business, science, or engineering.
· Publications at top-tier peer-reviewed conferences or journals.
· Depth and breadth in state-of-the-art computer vision and machine learning technologies.
· Good written and spoken communication skills.
· Experience with modern methods for parallelized processing of large, distributed datasets (e.g. Spark, Hadoop, Map-Reduce).
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.