Amazon is seeking an Applied Scientist to join its Demand Forecasting Science team, focusing on designing and deploying machine learning models for time series forecasting. The role involves innovative problem-solving and collaboration to enhance forecasting capabilities that impact Amazon’s global operations
Job Summary
Design new deep learning algorithms and creative approaches to push time series forecasting performance beyond the current state-of-the-art.
Work cross-functionally with product managers, software engineers, and scientists to build end-to-end solutions that are deployed at scale in Amazon’s production systems.
Engage with the broader scientific community at Amazon and beyond - collaborate with academic researchers, contribute to publications, and present your work at leading conferences (e.g., NeurIPS).
Matching Summary
Match Score: 85
Amazon is seeking an Applied Scientist to join its Demand Forecasting Science team, focusing on designing and deploying machine learning models for time series forecasting. The role involves innovative problem-solving and collaboration to enhance forecasting capabilities that impact Amazon’s global operations.
Salary
167,100.00 - 226,100.00 USD annually
Skills & Requirements
Must-have
state-of-the-art machine learning techniques
deep learning models for time series forecasting
novel neural network architectures
Python and PySpark
train, evaluate, and debug models
Nice-to-have
drive innovation
tackle real-world problems at massive scale
collaborate with academic researchers
present your work at leading conferences
Key Requirements
Requires PhD or equivalent in a quantitative field
Requires 3+ years of experience in applied machine learning