Amazon’s Long-Term Planning organization is looking for a Data Scientist to help invent the next generation of Amazon's Capacity and Constraint Management system - Automated Planning System (APS). APS will herald a a new era in Sales and Operations Planning (S&OP). APS emerges as a next-generation decision-making framework for Amazon's Worldwide (WW) fulfillment networks. In an industry first, APS seamlessly aligns Amazon's business controls by uniting leading-edge supply and demand forecasts with a state-of-the-art coordination framework – respecting the distributed ownership of business logic and outcomes. As the centralized planning system, APS takes charge of coordinating all fulfillment, inventory, and operational decisions, maximizing WW Long Term Free Cash Flow (LTFCF) over a 1-year horizon
The Long-Term Planning 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.
As a Data Scientist on the this team, you will build a deep understanding of Amazon's supply chain systems, lead innovation in our forecasting capabilities and build principled solutions to identify improvement opportunities in our supply chain using the latest machine learning techniques. You will also work with a team of Scientists, Product Managers, Business Intelligence Engineers and Software Engineers to research and build accurate predictive models and deploy automated software solutions to provide insights to business leaders at the most senior levels throughout the company. You will build models that make our data more actionable and help us make complex business decisions at scale.
To help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon -
http://bit.ly/amazon-scotKey job responsibilities
- Implement statistical and machine learning methods to 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
