Sr. Data Scientist, Device Economics

Job summary
Leverage Data Science to make multi-million dollar business decisions more Science and Data Driven.

In this role you will support Consumer Hardware device concepts (TVs, Kindles, Drones; you name it) from innovative idea inception to launch. You will work on a Data Science team tackling very difficult business questions and then scaling those Statistics and Econometrics solutions across Amazon Devices and beyond.


Key job responsibilities
Today Amazon Devices has too many different forecast models feeding into various decisions, especially how much inventory to build. To both reduce complexity and increase the quality of our forecasts and business decisions, in this role, you will own aggregating Science models together (existing and being built) to enable a single source of truth forecast that learns and grows and gets better over time. You will own the Science around how the models aggregate together. You will own relationships with the owners of the different existing Science models as inputs to understand those models and use that to combine them together effectively. You will work to simplify, identifying where Science models overlap with each other and where we can align on a single methodology further upstream or reduce overlap. You will participate in multi-million dollar decisions leveraging your work and use the feedback from such decision making to help inform the roadmap so we keep all keep learning both as a team and as individuals in our careers.

About the team
This role is on the Decision Science team, a group within Device Economics that support's Amazon's Devices and Services teams that build hardware and software ranging from Kindle Paperwhite to Blink Video Camera subscriptions to FireTV OS to the Amazon's Astro robot.

We own demand estimates and pricing recommendations of concept devices before customers know they exist but critical decisions need to be made such as whether to invest or not, how to price them, how many production lines to build with how much inventory, etc. We are a cross-functional Consulting team working to support these high Impact decisions at the most senior levels of leadership while then scaling the Data Science and Econometrics used across future decisions by embedding them into prototypes and internal to Amazon (for now) tools and Products.

Basic Qualifications

· Undergrad degree in Economics, Business, Finance, Engineering, Statistics, Computer Science, Mathematics, Supply Chain or related quantitative field
· 5+ years of work experience in a scientist or top down analytics role
· Experience with scripting and programming languages e.g. Python, SQL, R, etc. particularly around data manipulation
· Experience with statistical and machine learning models e.g. multinomial logistic regression, deep learning, etc.
· Comfort dealing with ambiguity
· Works backwards from the customer to solve their problems
· Excellent communication

Preferred Qualifications

· Masters, MBA, or PhD degree in Economics, Business, Finance, Engineering, Statistics, Computer Science, Mathematics, or related quantitiative field
· 8+ years of work experience in a scientist or top down analytics role
· 3+ years experience with scripting and programming languages e.g. Python, SQL, R, etc. particularly around data manipulation
· 3+ years experience with statistical and machine learning models e.g. multinomial logistic regression, deep learning, etc.
· Experience working with Data Scientists, Economists, Software Developers, and/or Product Managers
· Experience in Consumer facing Hardware or Services
· Experience in survey design and datasets




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.