Amazon - Westborough, MA
posted 2 months ago
Amazon Robotics is seeking a Business Intelligence Engineer to join our dynamic team in Westborough, Massachusetts. This role is pivotal in leveraging data to inform automation efforts and enhance supportability across the Amazon Robotics product portfolio. As part of the Support Automation Systems (SAS) group, you will engage in discovering support cost drivers and utilizing this data to develop tools and reports that automate manual processes for our support teams. You will collaborate closely with software and hardware engineers, as well as operations teams, to prioritize tasks, make feature trade-offs, and build new tooling and automation capabilities. Your analytical skills will be crucial as you conduct insightful analyses and communicate your findings through white papers and reports. The methodologies you will employ include design of experiments, statistical modeling, machine learning, financial analysis, and data visualization, all while relying on internal data engineering capabilities. In this role, you will work cross-functionally with engineering teams, program managers, and leaders throughout the organization to deliver projects that aim to understand support cost drivers and identify new ways to reduce them. You will utilize a combination of unified and disparate data sources to uncover insights, which will be delivered through decision-driving analytics white papers and automated data visualizations. Your collaboration with software engineers, technical program managers, and other business intelligence engineers will be essential in delivering world-class results for our customers. We are looking for a data professional who is passionate about delivering insightful and influential solutions to complex and ambiguous problems. You should be a versatile individual who can apply skills across data engineering, statistics, and data visualization to influence decision-making and improve automation across the organization. The ideal candidate will possess a holistic understanding of compound problems and how systems interconnect, allowing for the identification of new opportunities for improvement.