Job Description
DESCRIPTION
The Payment Acceptance and Experience (PAE) team at Amazon is responsible for building the most trusted, intuitive, and accessible payment experience globally. They enable new and existing shoppers anywhere in the world to pay on and off Amazon using their preferred payment method. The team also aims to delight non-shopping customers like sellers and developers with seamless, supported payment experiences. PAE is organized into several sub-charters that focus on different aspects of payment acceptance and experience, such as enabling local and regional payment methods, managing third-party partnerships, improving the payments customer journey, and driving payment accessibility.
The vision for the PAE Data Science (DS) team is to transform payments data into insight for business decisions, tools for controllership of payments metrics, and models to improve customer payment experiences, such as purchase success rate (PSR) and payment method adoption. We seek opportunities to improve PAE productivity through automation. We provide access to payments data that is accurate and timely. We build tools that reduce the effort needed to analyze data to make decisions. We develop Machine Learning (ML), artificial intelligence (AI), and economic models that that empower our customer facing features to delight customers.
We’re looking for an analytical builder to improve the Business Intelligence product that can bring greater insights for our stakeholders. This role offers exciting challenges and the opportunities to influence customer's paying experience on Amazon. A successful candidate loves problem solving, obsesses over data, and has a track record of using data to influence successful business decisions.
Key job responsibilities
Responsibilities include:
* Developing intelligent, insightful reporting on the key drivers of our business
* Interacting with internal stakeholders to lead weekly business reviews and deep dive analyses
* Ad-hoc project work supporting the re-invention of how we manage our business
* Enabling effective decision making by retrieving and aggregating data from multiple sources and compiling it into a digestible and actionable format
* Identifying process and system improvement opportunities by monitoring existing metrics, analyzing data, and partnering with internal teams
* Driving instrumentation as part of organization wide programs and enhancements
The expertise you’ll develop in the role:
* Ability to break down and solve complex problems
* Develop high impact solutions working with Data Scientists and Data Engineers focused on business intelligence
* Translate massive data sets into actionable business results
* Explain analysis results to senior stakeholders through verbal and written communications
* Drive scalable solution
A day in the life
Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan.
If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!
BASIC QUALIFICATIONS
- Bachelor's degree in statistics, business analytics, data analytics, data science, computer science or related field
- Experience with SQL, including class projects, personal projects, research, prior internships, or volunteer work
- Experience building and maintaining basic data artifacts (e.g., ETL, data models, queries)
- Experience with one or more analytics visualization tools (e.g., Excel, Tableau, QuickSight, MicroStrategy, PowerBI)
- Experience with statistical analysis, co-relation analysis
Job Tags
Full time, Temporary work, Internship, Seasonal work, Local area,