The Core sourcing science team, part of Amazon Supply Chain Optimization technologies seeks Applied Scientist with strong machine learning, analytical and communication skills to join our team. We develop sophisticated algorithms that involve learning from large amounts of data from diverse sources such Vendors, Transportation carriers, Amazon warehouses etc. in order to predict accurate arrival time of inventory into Amazon warehouses. These predictions are key input into optimization systems that source $100BB+ inventory into Amazon and plan labor for 100s of Amazon Fulfillment centers worldwide. These predictions will also be used to make back-in-stock promise to customers to increase selection and improve customer experience. With accurate prediction we drive down supply chain costs and improve labor planning enabling lower prices and better in-stock for our customers.
In a typical day, you will work closely with talented machine learning scientists, statisticians, software engineers, and business groups. Your work will include cutting edge technologies that enable implementation of sophisticated models on big data. As a successful data scientist in Inbound predictions team, you are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, can multi-task, and can credibly interface between technical teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems as well build new Machine learning solutions for inventory arrival predictions through collaboration with engineering, research, and business teams. 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.
Major responsibilities include:
· Analysis of large amounts of data from different parts of the supply chain and their associated business functions
. Develop new Machine learning (Deep learning, Reinforcement learning) models to improve accuracy of arrival predictions.
· 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
. Create prototypes and simulations to test devised solutions
. Work closely with engineers to integrate prototypes into production systems
· 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
Want to learn more about what it's like working for Amazon Supply Chain Optimization Technology? Check out these videos!
· Master’s or PhD degree in a quantitative field such as Machine Learning, Data Science, Statistics, Applied Mathematics.
· Fluency in a scripting or computing language (e.g. Python, Scala, C++, Java, etc.)
· 5+ years of relevant working experience in an analytical and model building role involving data extraction, analysis, statistical modeling, and communication
· 5+ years of experience with data querying languages (e.g. SQL, Hadoop/Hive)
· Experience processing, filtering, and presenting large quantities (Millions to Billions of rows) of data from different product groups and business functions
· Experience working with Machine Learning/Deep Learning for real world problems
Expertise in forecasting, deep learning, reinforcement learning, or other related fields
· Natural curiosity and desire to learn, a passion for solving real world problems
· Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment
· Superior verbal and written communication skills with the ability to effectively advocate technical solutions to scientists, engineering teams and business audiences.