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Senior Economist, Manager

Job ID: 1666297 | Amazon.com Services LLC

DESCRIPTION

Since its launch in 2014, Amazon Music has seen explosive user growth driven by integration with the highly successful Amazon Prime subscription program and Alexa voice-enabled devices. We are looking for a truly innovative Economist to work on disruptive ideas within the Amazon Music space. The role offers a unique opportunity to work in a fast growing Digital business on a central economist team focused on strategic and high-impact analysis for the business.

A day in the life
As a senior economist manager on the team, you will partner with the Music business and other scientists in the Digital space to deliver innovative modeling solutions to help take the Music business to the next level. This is an opportunity for a high-energy individual to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership.

This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. Experience in applied analytics is essential, and you should be familiar with modern tools for data science and business analysis. We are looking for a seasoned economist who is able to provide structure, including sourcing, defining, and leveling, around complex and varied business problems. We seek creative thinkers who can combine a strong economic toolbox with a desire to learn from others, and who know how to execute and deliver on big ideas.


About the hiring group
The Music Economist team is responsible for delivering transformational customer insights that drive marketing, content and product strategy for the Amazon Music streaming service. Team members are expected to independently own high-impact analysis which is presented to senior level business stakeholders. On the team, you will work with a high degree of independence in a collaborative environment of economists and other scientists, product managers, data engineers and software developers.

Job responsibilities
· Manage and grow a high-performing economist/science team
· Build econometric models, conduct statistical/machine learning analyses, or design experiments to measure the value of the business and its many features
· Independently identify new opportunities for leveraging economic insights and models in the Music business
· Develop and execute science products from concept, prototype to production incorporating feedback from customers, scientists and business leaders
· Write technical white papers and business-facing documents to clearly explain complex technical concepts to audiences with diverse business/scientific backgrounds




Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

BASIC QUALIFICATIONS

· PhD in Economics or closely related field
· PhD in Economics
· Proven experience in building statistical models using R, Python, STATA, or a related software, with a willingness to learn and develop additional skills in causal inference, structural econometrics, machine learning, large-scale scientific / distributed computing.
· 3+ years of post-PhD experience

PREFERRED QUALIFICATIONS

· Proficiency in Spark-Scala or Py-Spark
· Proven track record of managing a high-performing science team
· Ability to work effectively within an interdisciplinary science team of economists, applied scientists, software engineers, and data engineers
· Ability to communicate relevant scientific insights from data to senior business leaders, financial analysts, and product managers
· Background/post-graduate training in causal inference
· One or more publications in peer-reviewed statistical journals
· Experience in implementing modern machine-learning methods (e.g., boosted regression trees, random forests, neural networks)