Amazon’s Automated Inventory Management (AIM) team is looking for passionate, hard-working, and talented individuals to join our fast paced, stimulating environment to invent the future of business ownership with Technology.
The AIM team is part of the Supply Chain Optimization Technology (SCOT) Team within the Operations Organization. The charter of the SCOT team is to maximize Amazon’s return on our inventory investment in terms of Free Cash Flow and customer satisfaction. We accomplish this by determining how much inventory Amazon needs of each SKU and what to do if we have too much. This puts the Inventory Planning group at the nexus of operations, logistics, capacity planning, and our retail business teams.
As a Senior Data Scientist on the AIM team, you will research and develop long range forecasting models and solve real world problems using the latest machine learning techniques. You will research new modelling techniques that can discover and interpret causal effects in our supply chain systems at scale. You will work closely with senior business leaders, product managers and engineers to build innovative scientific solutions that impact millions of inventory decisions each day.
· Implement statistical and machine learning methods to forecast system behavior and solve complex business problems
· Research new ways to improve predictive and explanatory models
· Directly contribute to the design and development of automated prediction systems and ML infrastructure
· Build models that can detect supply chain defects and explain variance to the optimal state
· Collaborate with other researchers, software developers, and business leaders to define the scientific roadmap for this team
To help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon - http://bit.ly/amazon-scot
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age
· Bachelor or Master's degree in Statistics, Applied Mathematics, Operation Research, Economics or a related quantitative field
· 5+ years of hands-on experience in predictive modeling and machine learning
· Proficiency in model development, model validation and model implementation
· Proficiency working with Python/R
· Experience leading, mentoring, and growing teams of scientists
· Extensive knowledge and practical experience in several of the following areas: explainable machine learning, time-series forecasting, statistics, deep learning, causal inference.
· Experience processing, filtering, and presenting large quantities (Millions to Billions of rows) of data.
· Practical experience with big-data processing libraries, eg. Apache Spark, Apache Beam, Hive, Apache Pig, Hadoop or similar