Are you passionate about making Amazon the best place for Brands to tell their story, discover and engage new customers, drive traffic to their products and grow their business? Brand Merchandising and Content (BMC) is a team that owns building automated, self-service tools and services that allow Brands of all sizes to increase and optimize conversion via measurement of different content options, drive incremental traffic via marketing and social channels, and accelerate growth via actionable marketing recommendations with one-click actions. We use a variety of optimization approaches (e.g., ML, NLP, NLU, MAB, CV etc.) to drive the discovery, engagement and conversion needs of Brands.
You will have the opportunity to work on solving complex problems with Machine Learning in the areas of product lifecycle-based prediction, multi-product performance attribution, cold start, content experimentation and optimization. Using Amazon’s large-scale computing resources, you will rapidly experiment with Machine Learning solutions and productionalize models integrating with large-scale data processing pipelines with real-time metrics for feedback loops.Your work will directly benefit Brands and Customers, you will quantify and measure the impact using scientific tools.
As part of talented team of engineers and scientists, you will have the opportunity to create significant impact on our systems, our business and most importantly, our customers as we take on challenges that can revolutionize the e-commerce industry. We are looking for passionate, hard-working, and talented Applied Scientists who have experience building mission critical, big data applications powered by Machine Learning models that customers love. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.
· MS in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field.
· 4+ years of hands-on experience in predictive modeling and analysis.
· 3+ years hands-on experience programming in R, Python, Java or other similar programming languages.
· Proficiency in model development, model validation and model implementation for large-scale applications.
· Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to business audiences and engineering teams.
· PhD in Computer Science, Machine Learning, Operational Research, Statistics or a related quantitative field.
· Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations.
· Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
· Extensive experience applying theoretical models in an applied environment.
· Expertise on a broad set of ML approaches and techniques, ranging from Regression to Artificial Neural Networks.
· Expert in at least one scripting language (Python or similar) and one major programming languages (C++, Java, or similar).
· Strong fundamentals in problem solving, algorithm design and complexity analysis.
· Strong personal interest in learning, researching and creating new technologies with high business impact.