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Description

In the Worldwide Returns & ReCommerce (WW R&R) group at Amazon, we are dedicated to ‘making zero happen’ – zero cost of returns, zero waste, and zero defects – to benefit our customers, company, and environment. We are an agile and inclusive organization that constantly innovates to create long-term value by investing in our people and our planet, not simply focusing on the bottom line.

WW R&R includes business, product, operations, data, and software engineering teams, who together manage the lifecycle of returned and damaged products. In WW R&R, you will partner across these teams to help customers get the most value out of Amazon’s products; improve the customer returns experience; and reduce defects, waste, and cost in reverse logistics processes. You will be a leader, a builder, and an owner, collaborating cross-functionally with technical, operations, and business teams to design scalable and automated solutions to customer problems.

The candidate will focus on raising the bar on Refunds CX, through their deep-dive skills and customer insight generation capabilities.

Amazon is Earth’s most customer-centric company and in WW R&R, the Earth is our customer too. Come join us and innovate with the Amazon Worldwide Returns & ReCommerce.

Key job responsibilities
• Own the design, development, and maintenance of scalable solutions for ongoing metrics, reports, analyses, dashboards, etc. to support analytical and business needs.
• Work with team leadership to understand the customer needs and concerns as well as define solutions.
• Translate basic business problem statements into analysis requirements. Work with internal customers to define best output based on expressed stakeholder needs.
• Use analytical and statistical rigor to solve complex problems and drive business decisions.
• Develop queries and visualizations for ad-hoc requests and projects, as well as ongoing reporting.
• Design and drive experiments, A/B testing, outlier deep dives and form actionable recommendations. Manage the implementation of those recommendations.
• Write queries and output efficiently, and have in-depth knowledge of the data available in area of expertise. Pull the data needed with standard query syntax; periodically identify more advanced methods of query optimization. Convert data to make it analysis-ready.
• Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
• Monitor and troubleshoot operational or data issues in the data pipelines
• Review and audit existing ETL jobs and SQL queries.