招聘
必备技能
Machine Learning
Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems.
Intelligent Talent Acquisition (ITA)is looking for an economist with expertise in applying causal inference, experimental design, and/or causal machine learning techniques to topics in labor or related applied economics. They will collaborate with business partners to define and deliver economic thinking that guide strategic decisions. They will work closely with data scientists, research scientists and engineers to estimate and validate their models on large scale data, and will help business partners turn the results of their analysis to business actions that have a major impact.
Ideal candidates will own key inputs to all stages of research projects, including data requirements, model development, experimental design, and data analysis. They will be customer-centric, working closely with business partners to define key research questions, communicate scientific approaches and findings, listen to and incorporate partner feedback, and deliver successful solutions.
Key job responsibilities
The Economist will work with teammates (Data Scientists, Research Scientists (Industrial-Organizational Psychologists), Business Intelligence Engineers to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a Difference-in-Differences (DiD) analysis, estimate a structural model, building a statistical / regression model or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions.
A day in the life
The Economist will work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement the model, or writing and presenting a document with findings to business leaders. The economist will also conduct code and paper reviews, engage in science panel reviews and attend meetings to scope the work and/or present results. The economists will also manage and update their work through regular ASANA project updates.
Basic Qualifications
- PhD in economics or equivalent
Preferred Qualifications
- 2+ years of industry, consulting, government, or academic research experience
- Knowledge of at least one statistical software package such as R, Stata, Matlab, SAS
- Experience in applied macroeconomics, time series and macroeconometrics, economic theory, and quantitative methods
- Experience in data mining (SQL, ETL, data warehouse, etc.) and using databases in a business environment with large-scale, complex datasets
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. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, TX, Irving - 136,000.00 - 184,000.00 USD annually
USA, VA, Arlington - 136,000.00 - 184,000.00 USD 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
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0份报告
$108,330
年薪总额
基本工资
$43,332
股票
$54,165
奖金
$10,833
$75,831
$140,829
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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