We are looking for an experienced Data Analyst to join the Reverse Logistics Services team to help unlock insights which take our team to the next level. The ideal candidate will be excited about understanding and implementing new and repeatable processes, while providing data to improve the Returns transportation business. The successful candidate will have strong data mining and modeling skills and is comfortable facilitating ideas and working from concept through to execution. They will partner with various key stakeholders to deep dive into the business challenges and data to identify insights, providing recommendations for process improvement on a globally scalable level. This role requires an individual with excellent analytical abilities as well as strong business acumen.


Key job responsibilities
-Retrieving and analyzing data using Excel, SQL, and other data management systems
-Monitoring existing metrics, analyzing data and partnering with internal/external teams to identify process and system improvement opportunities
-Design, develop and maintain scalable, automated, user-friendly systems, reports, or dashboards to enable stakeholders to manage the business and drive effective decisions
-Prepare and deliver business requirements reviews to leadership teams
-Excellent writing skills, to create artifacts easily digestible by business and tech partners.
-Be self-driven, and show ability to deliver on ambiguous projects with incomplete data


About the team
At Amazon Worldwide Returns & ReCommerce (WW R&R), we aspire to zero: zero cost of returns, zero waste, and zero defects.
We are an agile, innovative organization dedicated to ‘making zero happen’ to benefit our customers, company, and environment. We are constantly innovating to create long-term value at Amazon by investing in the future and focusing on the planet, not just on the bottom line. WW R&R includes business, product, program, operational, data, and software engineering teams, who together manage the lifecycle of returned and damaged products.