Sr. Research Scientist

Job ID: 1590897 | Amazon.com Services LLC

DESCRIPTION


SCOT Network Topology Optimization science team focuses on research areas and tools that determine Amazon outbound transportation network design as we transition to relying on our internal carrier network and accelerate one-day delivery speed. There are various strategic questions the team is attempting to answer, such as: what is the impact of inventory placement on outbound transportation cost and delivery speed? What is the optimal transportation network design given processing capacity constraints? How can we forecast accurately fulfillment pattern for different customer clusters?. If you are interested in diving into a multi-discipline, high impact space this team is for you. So far, we utilized models from various science disciplines such as: Mixed Integer optimization, Random Forest (or other ML techniques), stochastic/probabilistic model, economic analysis, to name a few.

In addition to transportation network, we also use forecasting and optimization techniques to evaluate new facilities recommendation for long term estimates, We use machine learning to approximate the network, and simulation of how our choices will perform. The team is a mixture of Software Engineers, Operations Research Scientists, Applied Scientists, Business Intelligence Engineers and Product Managers.

We are looking for a Sr. Research Scientist who has a deep knowledge of analyzing large-scale fulfillment data using Machine learning and optimization. Those who are strong in forecasting space should have a breadth of other ML experience in a production environment using techniques. This role will focus on expanding our reach to analyze various fulfillment and transportation for Amazon's supply chain network worldwide.

To help describe some of our challenges, we created a short video about Supply Chain Optimization at Amazon - http://bit.ly/amazon-scot

Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age

BASIC QUALIFICATIONS

· PhD in a quantitative field such as Mathematics, Statistics, Physics, Engineering, Computer Science, Machine Learning, Economics and 5+ years of industry experience OR MS or greater in a quantitative field and 9+ years of experience (after graduation)
· Strong coding and problem-solving skills in at least one programming language such as Python, Java, SQL, etc.
· Familiar with AWS environment, such as S3, Sage Maker and other
· Experience with linear Optimization or Stochastic Optimization model to make big-impact decision making. Familiar with using CPLEX/XPRESS/Gurobi
· Sound theoretical understanding of broad machine learning concepts, with deep and demonstrable expertise in at least one topic or application of machine learning.

PREFERRED QUALIFICATIONS

· Experience with fully automated machine training (e.g. automatic re-training, automatic testing) on techniques such as Random Forest, Regression (Linear), Time-Series (ARIMA) and Neural network (LSTM, CNN) using 1+GB datasets.
· Experience with high-impact decisions (> $50M)
· Experience with forecasting in production, providing bridging decisions and explanation of models and its impact
· Experience with consulting senior management on benefits of new supply chain design based economic ROI analysis using stochastic optimization techniques.
· Experience building optimization model using XPRESS/Mosel, Gurobi or CPLEX. Fine tuning and designing complex mathematical problem into various decomposition algorithm.
· Experience writing production-quality code using collaborative process such as Git and AWS.