Business Intelligence Engineer - Community Trust Amazon
Seattle, WA

Job Description

Community Trust is responsible for building customer trust and reducing abuse on Amazon's Community features, including Customer Reviews, Questions and Answers, Lists, Spark and Customer Profiles. By driving customer trust in user generated content, we increase long term customer engagement on Amazon. Upholding customer trust is multifaceted but impactful for Amazon. Our team is passionate about human motivation and behavior and uses business intelligence, machine learning, classification algorithms and scalable platform services to have a cross-org impact every day. We are inherently scrappy and innovative, experimenting with data about millions of contributions worldwide to identify new genuine and malicious content and contribution patterns. We evaluate user research, customer experience knowledge, data, and cross-Amazon abuse expertise to ensure other teams think about customer trust.

The role requires customer focus, creativity, a data-first orientation and experience with performance and scale. You will have the opportunity to make a difference in a fast paced, dynamic environment where your ability to deep dive, communicate, collaborate and drive changes will be a key. You will play a very important role in ensuring a great customer experience improving customer trust in our user generated content features with an eye on automation to help developing scalable and robust solutions.

A successful candidate knows and loves working with analytic tools, is comfortable accessing and working with data from multiple sources, and can partner with internal stakeholders to help drive the business.

Responsibilities:

* Interface with business customers, gathering requirements and delivering business analysis.
* Build customer trust data analysis projects to better understand how shoppers, contributors and sellers engage with Community content.
* Produce written recommendations and insights for key stakeholders that will help shape effective strategies.
* Simplify and automate reporting, audits, and other data activities; build solutions to have maximum scale and self-service ability by stakeholders.
* Continually improve ongoing reporting and analysis processes and ML models