招聘
As the Economist for Customer Promise, you will be responsible for leading the research, econometric modeling, and analysis to understand customer preferences that inform how the business operates. This entails developing analytic tools and economic models - including generative AI agents - that take into account inventory, fulfillment center capabilities, carrier capabilities, customer preferences, and economic impacts to determine customer promise.
You will design, build, and rigorously validate GenAI agents against traditional econometric models and production experiments to assess their accuracy, interpretability, and operational impact. The models and agents you develop will drive changes in transportation and fulfillment networks.
You will work with a diverse scientific team including computer scientists, machine learning engineers, and statisticians as well as other economists. You will build statistical models and AI-powered agents using world-class data systems to solve business problems in a fast-paced environment, continuously evaluating new methodologies against established econometric approaches to advance the state of the art in promise optimization.
Key job responsibilities
Conduct economic analysis and develop models that apply mathematical, econometric, and statistical techniques to business problems, including estimates, optimizations, and forecasts using established methodologies in your specialty area
Partner with business stakeholders to understand their challenges and translate business questions into technical economic frameworks that deliver workable solutions
Build and validate economic models by ensuring data quality, testing results using standard practices, and taking responsibility for correct implementation and the impact your analysis has on business decisions
Communicate findings clearly through technical documents, reports, and presentations to leaders and colleagues, conveying your reasoning and limitations of your analysis with transparency
Collaborate with other economists and technical teams to embed economic perspectives into projects and contribute to team goals and project-related metrics
A day in the life
In this role, you'll focus on building economic models and conducting data analysis that support critical business decisions. You might start your day by meeting with a business partner to understand a pricing question or operational challenge, then translate that into a technical framework. You'll spend time working with data analysis software like R, Matlab, or Stata—and potentially Python—to develop models, validate results, and ensure your analysis aligns with project goals. Throughout the day, you'll document your findings in clear technical reports, present your reasoning to colleagues, and collaborate with other economists to embed economic thinking into broader initiatives. You'll also stay current with developments in your economics specialty and participate in team discussions about methodology and best practices.
About the team
At Amazon, we are the most customer-centric company on earth. If you'd like to help us build the place to find and buy anything online, this is your chance to make history. To get there, we need exceptionally talented, bright, and driven people. We are looking for a dynamic, organized self-starter to join as an Economist for Customer Promise.
The Customer Promise team seeks to identify the optimal delivery speed for whatever a customer wants. We believe that finding an optimal promise and living up to it consistently improves our customer experience because we increase customer's confidence and trust in Amazon as the one, best option to get what you want, when you want it.
Basic Qualifications
- PhD in Economics, Quantitative Marketing or related field
Preferred Qualifications
- Experience working with data warehousing, data quality
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.
The base salary range for this position is listed below. As a total compensation company, Amazon's package may include other elements such as sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription, basic life & AD&D insurance), Registered Retirement Savings Plan (RRSP), Deferred Profit Sharing Plan (DPSP), paid time off, and other resources to improve health and well-being. We thank all applicants for their interest, however only those interviewed will be advised as to hiring status.
CAN, BC, Vancouver - 98,700.00 - 164,800.00 CAD annually
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关于Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
员工数
Seattle
总部位置
$1.5T
企业估值
评价
2.9
10条评价
工作生活平衡
2.8
薪酬
3.7
企业文化
2.5
职业发展
2.3
管理层
2.1
35%
推荐给朋友
优点
Good pay and compensation
Strong benefits package
Flexible scheduling options
缺点
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
薪资范围
4个数据点
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Junior/L3 · Data Scientist L4
0份报告
$181,968
年薪总额
基本工资
-
股票
-
奖金
-
$154,672
$209,264
面试经验
10次面试
难度
3.7
/ 5
时长
21-35周
录用率
20%
体验
正面 10%
中性 10%
负面 80%
面试流程
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
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