Job
Description
Want to join the Earth s most customer centric company? Do you like to dive deep to understand problems? Are you someone who likes to challenge Status Quo? Do you strive to excel at goals assigned to you? If yes, we have opportunities for you. Global Operations Artificial Intelligence (GO-AI) at Amazon is looking to hire candidates who can excel in a fast-paced dynamic environment. Are you somebody that likes to use and analyze big data to drive business decisions? Do you enjoy converting data into insights that will be used to enhance customer decisions worldwide for business leaders? Do you want to be part of the data team which measures the pulse of innovative machine vision-based projects? If your answer is yes, join our team. GO-AI is looking for a motivated individual with strong skills and experience in resource utilization planning, process optimization and execution of scalable and robust operational mechanisms, to join the GO-AI Ops DnA team. In this position you will be responsible for supporting our sites to build solutions for the rapidly expanding GO-AI team. The role requires the ability to work with a variety of key stakeholders across job functions with multiple sites. We are looking for an entrepreneurial and analytical program manager, who is passionate about their work, understands how to manage service levels across multiple skills/programs, and who is willing to move fast and experiment often. Ability to maintain and refine straightforward ETL and write secure, stable, testable, maintainable code with minimal defects and automate manual processes. Proficiency in one or more industry analytics visualization tools (e.g. Excel, Tableau/Quicksight/PowerBI) and, as needed, statistical methods (e.g. t-test, Chi-squared) to deliver actionable insights to stakeholders. Building and owning small to mid-size BI solutions with high accuracy and on time delivery using data sets, queries, reports, dashboards, analyses or components of larger solutions to answer straightforward business questions with data incorporating business intelligence best practices, data management fundamentals, and analysis principles. Good understanding of the relevant data lineage: including sources of data; how metrics are aggregated; and how the resulting business intelligence is consumed, interpreted and acted upon by the business where the end product enables effective, data-driven business decisions. Having high responsibility for the code, queries, reports and analyses that are inherited or produced and having analyses and code reviewed periodically. Effective partnering with peer BIEs and others in your team to troubleshoot, research root causes, propose solutions, by either take ownership for their resolution or ensure a clear hand-off to the right owner. About the team The Global Operations Artificial Intelligence (GO-AI) team is an initiative, which remotely handles exceptions in the Amazon Robotic Fulfillment Centers Globally. GO-AI seeks to complement automated vision based decision-making technologies by providing remote human support for the subset of tasks which require higher cognitive ability and cannot be processed through automated decision making with high confidence. This team provides end-to-end solutions through inbuilt competencies of Operations and strong central specialized teams to deliver programs at Amazon scale. It is operating multiple programs and other new initiatives in partnership with global technology and operations teams. 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience Experience with data visualization using Tableau, Quicksight, or similar tools Experience with data modeling, warehousing and building ETL pipelines Experience in Statistical Analysis packages such as R, SAS and Matlab Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
