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Economist , Economic Decision Science

Amazon

Economist , Economic Decision Science

Amazon

London, GBR

·

On-site

·

Full-time

·

1w ago

We are looking for an Economist to work on exciting and challenging business problems related to Amazon Retail’s worldwide product assortment. You will build innovative solutions based on econometrics, machine learning, and experimentation. You will be part of a interdisciplinary team of economists, product managers, engineers, and scientists, and your work will influence finance and business decisions affecting Amazon’s vast product assortment globally.

If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you.

  • Key job responsibilities
  • Work on a challenging problem that has the potential to significantly impact Amazon’s business position
  • Develop econometric models and experiments to measure the customer and financial impact of Amazon’s product assortment
  • Collaborate with other scientists at Amazon to deliver measurable progress and change
  • Influence business leaders based on empirical findings

Basic Qualifications

  • PhD in business economics, engineering, analytics, mathematics, statistics, information technology or equivalent
  • Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi-experiments, and data science workflows

Preferred Qualifications

  • Experience communicating results to senior leadership, or experience in building financial and operational reports/data sets that inform business decision-making
  • Strong research track record

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

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.

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About Amazon

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

Employees

Seattle

Headquarters

$1.5T

Valuation

Reviews

2.9

10 reviews

Work-life balance

2.8

Compensation

3.7

Culture

2.5

Career

2.3

Management

2.1

35%

Recommend to a friend

Pros

Good pay and compensation

Strong benefits package

Flexible scheduling options

Cons

Poor management and leadership

Limited growth and promotion opportunities

High stress and demanding work environment

Salary Ranges

4 data points

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 reports

$181,968

total per year

Base

-

Stock

-

Bonus

-

$154,672

$209,264

Interview experience

10 interviews

Difficulty

3.7

/ 5

Duration

21-35 weeks

Offer rate

20%

Experience

Positive 10%

Neutral 10%

Negative 80%

Interview process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Loop

6

Team Matching

7

Offer

Common questions

Coding/Algorithm

System Design

Behavioral/STAR

Leadership Principles

Technical Knowledge