Data Science - Simulations, SCOT FO Simulations (LITMUS)

The Supply Chain Optimization Technologies (SCOT) team builds technology to automate and optimize Amazon’s supply chain of physical goods. We seek a Data Scientist with strong analytical and communication skills to join our team. SCOT manages Amazon's inventory under uncertainty of demand, pricing, promotions, supply, vendor lead times, and product life cycle. We optimize complex trade-offs between customer experience, inventory costs, fulfillment costs, fulfillment center capacity, etc. We develop sophisticated algorithms that involve learning from large amounts of data such as prices, promotions, similar products, and other data from our product catalog in order to automatically act on millions of dollars’ worth of inventory weekly and establish plans for tens of thousands of employees. As a Data Scientist, you will contribute to the research community, by working with other scientists across Amazon and our Supply Chain, as well as collaborating with academic researchers and publishing papers both internally and externally.


Key job responsibilities
Major responsibilities include:
- Analysis of large amounts of data from different parts of the supply chain and their associated business functions
- 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
- 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 (Python, R, Scala, etc.) for analyzing data and building statistical and machine learning models and algorithms


A day in the life
As a Data Scientist in SCOT, you will be tasked to understand and work with cutting edge research to enable the implementation of sophisticated models on big data. As a successful data scientist in the SCOT team, you are an analytical problem solver who enjoys diving into data from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between scientists, engineers and business stakeholders. Your expertise in synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication will enable you to answer specific business questions and innovate for the future.


About the team
Have you ever ordered a product on Amazon, and when that box-with-the-smile arrives, you wonder how it got to you so fast? Wondered where it came from and how much it would have cost Amazon? If so, Amazon’s Supply Chain Optimization Technologies (SCOT) team is for you. We build systems to peer into the future and estimate the distribution of tens of millions of products every week to Amazon’s warehouses in the most cost-effective way. This team focuses on saving hundreds of millions of dollars using cutting edge science, machine learning, and scalable distributed software on the Cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing, and supply. Watch this short video for more on SCOT: http://bit.ly/amazon-scot

Within SCOT, Fulfillment Optimization (FO) Simulations team provides a mechanism to evaluate the forward or backward-looking impact of a change to the input, logic, or configuration of outbound fulfillment systems in SCOT.

FO Simulations integrates with production services that decide how Amazon fulfills customer orders and simulates multiple alternate realities to calculate the impact of changing algorithms and inputs, such as, the simulations capture the network effects of planning and execution decisions. The network effect is the idea that if we are simulating an altered network, where planning decisions are being made differently from what happens in production, we capture and accumulate the outcomes of those different decisions across the planning services. The standardized impact measurement focuses on key business metrics such as promise speed, fulfillment cost, volume flows and resource capacity consumption patterns.