Intern - Economics, WW Returns & ReComm Tech& Inn

As an Intern - Economics at Amazon, you will work extensively with data sets to perform applied econometric analysis. You'll develop time series forecasting solutions, collaborate with seasoned economists and business intelligence teams, and gain hands-on experience in data management and analysis, all of which are invaluable for your future career in economics.

To apply for the Intern - Economics position at Amazon, you should be a PhD student in Economics who is in their third year or more, and not currently on the job market. Familiarity with time series forecasting, as well as programming languages like Python or R, is essential to excel in the role.

During your internship as an Intern - Economics at Amazon, you'll gain skills in building and managing data sets, applied econometric analysis, and working across various teams with economists and scientists. You’ll also learn about time series forecasting at scale, and better your programming skills, particularly in Python or R.

Amazon is deeply committed to fostering a diverse and inclusive workplace for all employees, including interns in the Intern - Economics role. The company promotes equal opportunities regardless of race, gender, sexual orientation, or any other legally protected status, ensuring that every intern can thrive and contribute their unique perspective.

The transition rate from an intern to a full-time economist at Amazon is quite impressive, with approximately 85% of previous interns converting to full-time roles. This significant statistic showcases the value of the internship experience and its potential to launch your career.

When answering this question, highlight any relevant projects you've worked on, the methodologies you've employed, and your familiarity with tools such as Python or R. Mention specific techniques you've used, such as ARIMA or exponential smoothing, and how you've validated your models.

Discuss your experiences with data management tools and programming languages. Provide an example of how you structured, cleaned, and analyzed large data sets in previous projects or coursework to minimize errors and draw meaningful insights.

Your answer should encompass a brief overview of Bayesian inference and its applications. If you've worked on projects involving Bayesian methods, discuss how you applied them and the outcomes, showcasing your relevant experience.

Express your enthusiasm for Amazon's commitment to customer satisfaction and innovation. Highlight how the role aligns with your academic and career goals, particularly in data-driven decision-making and econometric analysis.

Choose a relevant academic challenge that showcases your problem-solving skills. Describe the situation, your thought process, the actions you took, and the results. This demonstrates your resilience and analytical capabilities.

Discuss your level of proficiency with Python or R, mentioning specific projects where you used these languages. Be sure to emphasize any particular libraries or packages you are comfortable with that are relevant to the role.

List the statistical methods you have experience with, such as regression analysis, hypothesis testing, or econometric modeling. Give examples of how you've applied these methods in your coursework or research projects.

Explain your approach to task prioritization, such as the tools you use for project management, criteria for deadlines, or methods for assessing the urgency versus importance of each task. Share a specific example to illustrate your point.

Discuss the importance of accurate forecasting in anticipating market trends, managing supply chain logistics, and making data-informed decisions. Relate this to your understanding of Amazon's operations and how you could contribute.

Share a specific example where you collaborated with others on a project, detailing your role, the dynamics of the team, and how you contributed to achieving a common goal. This reflects your ability to work well within a team setting.