Senior Data Scientist, SCOT Inventory Placement

Come and be a part of Amazon's amazing growth story! If you are looking for an opportunity to solve deep technical problems and build innovative solutions in a fast paced environment working with smart, passionate team members, this might be the role for you! Amazon Supply Chain Optimization Technology (SCOT) powers Amazon’s fulfillment network, determining how much of a given product is needed at locations around the world in order to ensure product availability while maintaining optimal inventory levels at each storage location. This includes strategically placing vendor orders and moving inventory across our network to serve customer demand as quickly as possible at reduced cost. (Learn more about SCOT: http://bit.ly/amazon-scot)

The Inventory Placement team is seeking for Sr Data Scientist with strong analytical and communication skills to join our team. As a Senior Data Scientist for Inventory Placement, you will be responsible for driving improvements in how our inventory is distributed, stored, and replenished across our supply chain network. Your expertise in machine learning, statistical modeling, and optimization will enable our system teams to make smarter, data-backed decisions to finally ensure that products are placed in the right locations, at the right time, and in the right quantities. You will work closely with cross-functional teams in supply chain, engineering, and product to create scalable solutions that improve inventory efficiency and reduce operational costs.


Key job responsibilities
- Analysis of large amounts of data from different parts of the supply chain and their associated business functions using SQL.
- Improving upon existing machine learning methodologies by developing new data sources, developing and testing model enhancements, running computational experiments, and fine-tuning model parameters for new models
- Measure the impact of new features in our systems to own data driven decisions for future investments.
- Formalizing assumptions about how models are expected to behave, creating definitions of outliers, developing methods to systematically identify these outliers, and explaining why they are reasonable or identifying fixes for them
- Communicating verbally and in writing to business customers with various levels of technical knowledge, educating them about our research, as well as sharing insights and recommendations
- Utilizing code (SQL, Python, R, etc.) for analyzing data and building statistical and machine learning models and algorithms

About the team
Our Inventory Placement team owns the systems that decide where in Amazon's fulfillment network to put inventory for the millions of products that Amazon sells. We build optimization models to make these placement decisions driven by signals such as forecasted customer demand, the cost and speed of shipping from each warehouse to each customer, and the available capacity at various points in our network. Inventory placement is central to achieving Amazon's objectives to minimize the cost of fulfillment while offering selection to customers at the fastest possible delivery speeds. Our systems are built entirely in-house, and are on the cutting edge in automated large scale supply chain planning and optimization systems. We're simultaneously developing the science of supply chain planning and solving some of the toughest computational challenges at Amazon.