Description

Where will Amazon's growth come from in the next year? What about over the next five? Which product lines are poised to quintuple in size? Are we investing enough in our infrastructure, or too much? How do our customers react to changes in prices, product selection, or delivery times? These are among the most important questions at Amazon today. The Topline Forecasting team in the Supply Chain Optimization Technologies (SCOT) group is looking for innovative, passionate and results-oriented Data Scientists to answer these questions. You will have an opportunity to own the long-run outlook for Amazon’s global consumer business and shape strategic decisions at the highest level. The successful candidate will be able to formalize problem definitions from ambiguous requirements, build forecasting models using Amazon’s world-class data systems, and develop cutting-edge solutions for non-standard problems.

Amazon is an Equal Opportunity Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.

Key job responsibilities

• Develop forecasting models or improve existing approaches using scalable techniques.

• Extract data for analysis and model development from large, complex datasets.

• Closely work with engineering teams to build scalable, efficient systems that implement prototypes in production.

• Distill problem definitions from informal business requirements and communicate technical solutions to senior business leaders.

• Drive innovation and best practices in applied research across the Amazon research science community.

We are open to hiring candidates to work out of one of the following locations:

Bellevue, WA, USA | New York, NY, USA

Basic Qualifications

• 5+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience

• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience

• Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Bachelor's degree and 8+ years of professional or military experience

• Experience with statistical models e.g. multinomial logistic regression

Preferred Qualifications

• 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience

• Experience managing data pipelines

• Experience as a leader and mentor on a data science team

• If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $127,300/year in our lowest geographic market up to $247,600/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.