Senior Data Scientist, Amazon

• 5+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science, or Bachelor's degree and 8+ years of professional or military experience
• Experience with statistical models e.g. multinomial logistic regression

Desirable

• Amazon's Supply Chain Optimization Technologies (SCOT) group is at the forefront of applying machine learning to transformative real-world problems
• We are seeking a talented Senior Applied Scientist with Reinforcement Learning (RL) experience to join our team, where you will be focused on making significant contributions to the field, through hands-on research in RL
• As a Data Scientist in SCOT, you will be working alongside thought science and engineering leaders, contributing to academic research and complex, real-world applications
• Your work will directly influence Amazon's global inventory planning systems, shaping decisions that affect billions of dollars worth of inventory and a wide array of product lines
• 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
• 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