The Worldwide Defect Elimination (WWDE) Team within Amazon Customer Service is seeking a highly skilled economist to estimate the customer impact of each Customer Service action. Your analysis will assist teams across Amazon to prioritize defect elimination efforts and optimize how we respond to customer contacts. You will partner closely with our product, program, and engineering teams to deliver your findings to users via systems and dashboards that guide Customer Service planning and policies.
Key job responsibilities
- Develop causal, economic, and machine learning models at scale.
- Engage in economic analysis; raise the bar for research.
- Inform strategic discussions with senior leaders across the company to guide policies.
A day in the life
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!
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:
* Medical, Dental, and Vision Coverage
* Maternity and Parental Leave Options
* Paid Time Off (PTO)
* 401(k) Plan
About the team
The WWDE team's mission is to understand and resolve all issues impacting customers and connect all organizations in Amazon to customer experiences. Our vision is to be the ultimate steward of the Voice of the Customer (VoC), empowering CS and Amazon teams to easily measure, listen, and act on customer feedback. The team broadly supports defect detection, root cause identification, and resolution to earn customer trust. The Customer Service Economics & Optimization team is a force multiplier within this group. Through causal analysis, we estimate the effectiveness of our efforts to delight the customer.- PhD in economics or equivalent
- 2+ Years of Experience Developing Causal Models
- Experience in econometrics, economic theory, and quantitative methods
- Excellent written and verbal communication skills- Experience in implementing modern machine-learning methods (e.g., boosted regression trees, random forests, neural networks)
- Experience working with very large, disparate data sets and big data tools such as Spark
- Knowledge of data analysis languages (e.g., R, Stata, Python, etc.) or other statistical software
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit
https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $212,800/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit
https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
