The Economics Technology team (ET) applies Machine Learning, Causal Inference, and Econometric/Economic Methodologies to derive actionable insights about the complex economy of Amazon's retail business. We also develop Statistical Models and Algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Engineers, and Scientists incubating and building day one solutions using cutting-edge technology, to solve some of the toughest business problems at Amazon.
This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. If you are interested in Machine Learning, Reinforcement Learning, large-scale, low-latency distributed systems and can build the software that use the models, this is the role you have been looking for.
We are a Day 1 team, with a charter to be disruptive through the use of ML and bridge the Science and Engineering gaps that exist for engineers today. You will start on green field projects working with Principal Scientists to bring our models to life. We are an inclusive team, and are looking for SDEs that aren't averse to learning and building predictive models alongside our scientists.BASIC QUALIFICATIONS
• Bachelor's Degree in Computer Science or related field
• Computer Science fundamentals in object-oriented design
• Computer Science fundamentals in data structures
• Computer Science fundamentals in algorithm design, problem solving, and complexity analysis
• Proficiency in, at least, one modern programming language such as Java, C++, C#
• Basic understanding of machine learning and econometricsPREFERRED QUALIFICATIONS
• Experience building large-scale machine-learning systems
• Experience with Big Data technologies such as AWS, Hadoop, Spark
• Experience building complex software systems that have been successfully delivered to customers
• Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
• Experience with machine learning, data mining, and/or statistical analysis tools
Software and Programming