Data Scientist

Key Skills

Java Programming, Python Programming, SQL, Apache Hadoop, Machine learning techniques, Deep Learning, Apache Hive, R Programming, Statistical modeling, Data Analysis

Job Description

Business/Team Introduction

The Supply Chain Optimization Technologies (SCOT) team builds technology to automate and optimize Amazon’s supply chain of physical goods. We seek a 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 such as prices, promotions, similar products, and other 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 , you will contribute to the research community, by working with other s across Amazon and our Supply Chain, as well as collaborating with academic researchers and publishing papers. SCOT also engages in cutting edge research that we try to share with the community. Recent work from SCOT includes papers presented at the NIPS 2017 Time Series Workshop, SSRN, KDD 2018 Time Series Workshop, and ICML 2018 Deep Generative s Workshop.

Responsibilities

As a in SCOT, will be tasked to understand and work with bleeding edge research to enable the implementation of sophisticated s on big . As a successful in the SCOT team, you are an analytical problem solver who enjoys diving into from various businesses, is excited about investigations and algorithms, can multi-task, and can credibly interface between s, 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.

Major responsibilities include:

• Analysis of large amounts of from different parts of the supply chain and their associated business functions
• Improving upon existing machine learning methodologies by developing new sources, developing and testing enhancements, running computational experiments, and fine-tuning parameters for new s
• Formalizing assumptions about how s 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 (, R, , etc.) for analyzing and building statistical and machine learning s and algorithms

BASIC QUALIFICATIONS

• Master’s or PhD degree in a field such as Machine Learning, Science, Statistics, Applied Mathematics, Physics, Computer Science, or Economics
• Fluency in a scripting or computing language (e.g. , , , , etc.)
• 2+ years of relevant working experience in an analytical role involving extraction, analysis, statistical ing, and communication
• 2+ years of experience with querying languages (e.g. , Hadoop/Hive)
• Experience processing, filtering, and presenting large quantities (Millions to Billions of rows) of from different product groups and business functions
• Experience working with Machine Learning/Deep Learning for real world problems

PREFERRED QUALIFICATIONS

• Expertise in reinforcement learning and/or deep learning
• 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 s, engineering teams and business audiences
• Depth and breadth in knowledge. Excellent machine learning, statistical analysis and problem-solving skills