SCOT Network Topology science team focuses on research areas and tools that determine Amazon outbound 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 placement on outbound cost and delivery speed? What is the optimal network design given 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 , Random Forest (or other ML techniques), /probabilistic model, economic analysis, to name a few.
In addition to network, we also use and techniques to evaluate new facilities recommendation for long term estimates, We use 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 knowledge of analyzing fulfillment data using and . Those who are strong in 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 for Amazon's network worldwide.
To help describe some of our challenges, we created a short video about at Amazon - http://bit.ly/amazon-scot
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation / Age
· PhD in a quantitative field such as Mathematics, Statistics, Physics, Engineering, Computer Science, , 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 such as Python, Java, SQL, etc. · Familiar with AWS environment, such as S3, Sage Maker and other · Experience with linear or model to make big-impact decision making. Familiar with using CPLEX/XPRESS/Gurobi · Sound theoretical understanding of broad concepts, with and demonstrable expertise in at least one topic or application of .
· Experience with fully automated 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 in production, providing bridging decisions and explanation of models and its impact · Experience with consulting senior management on benefits of new design based economic ROI analysis using techniques. · Experience building 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.