Research Scientist Forecasting

Job ID: 988972 | Services LLC


Where will Amazon's growth come from in the next year? What about over the next five? What are the drivers and factors of such growth? 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 Forecasting team in the Supply Chain Optimization Technologies (SCOT) organization is dedicated to answering these questions using quantitative and statistical methods. We develop cutting edge data pipelines, build accurate predictive models, and deploy automated software solutions to provide forecasting insights to business leaders at the most senior levels throughout the company. We are looking for a talented, driven, and analytical researcher to help us answer these (and many more) questions.

The Top Line Science team leads the way in developing innovative models, algorithms and strategies that will help us gain insights into how our business will grow and what will the drivers of such growth. These predictive models and insights will be based along products and product categories, customer segments, regions and locations, etc.

This Research Scientist role will explore and develop innovative quantitative approaches and models, generate features, test hypotheses, design experiments, build predictive models, and work with very large complex data sets in order to make predictions and forecasts. These forecasts and insights will provide a foundation of the highest level of visibility and importance for Amazon's financial and operational planning. The successful candidate will be a problem solver who enjoys diving into data, is excited by difficult modeling challenges, and possesses strong communication skills to effectively interface between technical and business teams, working together with Software Engineers, Product Managers, Business Analysts and other Scientists.

Key Responsibilities:
· Research, develop and build predictive models and forecasting systems for our top line dimensions
. Analyze and research features and engineer features that help support predictive models and forecasting systems
. Provide insights by analyzing historical data
· Constructively critique peer research and mentor junior scientists and engineers.
· Create experiments and prototype implementations of new learning algorithms and prediction techniques.
· Collaborate with engineering teams to design and implement software solutions for science problems.
· Contribute to progress of the Amazon and broader research communities by producing publications.

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


· Ph.D./M.S. in Computer Science, Machine Learning, Engineering, Statistics or a related quantitative field
· 2+ years of hands-on experience in predictive modeling and analysis
· 2+ years hands-on experience in a high level programming language (Python, Perl, Scala, Java, C#, C++ or other similar language)
· 2+ years of experience in SQL
· Proficiency in model development, model validation and model implementation for large-scale applications
· Ability to convey mathematical results to non-science stakeholders
· Strength in clarifying and formalizing complex problems
· Significant peer-reviewed scientific contributions in premier journals and conferences


· Ph.D. in Computer Science, Machine Learning, Engineering, Statistics or a related quantitative field
· 4+ years of practical experience applying ML to solve complex problems in an applied environment
· Strong CS fundamentals in data structures, problem solving, algorithm design and complexity analysis
· 1+ years professional experience in software development
· 2+ years in Spark, MapReduce, and any other big data programming language
· Experience with defining research and development practices in an applied environment
· Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts