Applied Scientist, Demand Forecasting

Amazon

United States, WA, United States
142,800.00 - 193,200.00 usd annually py
**
Foundation models for time series
Deep learning architectures
Transfer learning and zero-shot forecasting
** Amazon is seeking an Applied Scientist for its Demand Forecasting team to design and implement innovative deep learning architectures for large-scale time series forecasting. The role involves collaborating with software engineers to develop and deploy models that directly influence significant inventory decisions while contributing to the scientific community through research publications. **

Job Summary

  • Design and build large-scale foundation models that generalize across an enormous and diverse catalog of products, geographies, and business contexts.
  • Develop new model architectures and training methodologies that push the boundaries of what foundation models can learn from vast, heterogeneous time series data.
  • The models you design here will ship to production and directly influence hundreds of millions of dollars in automated inventory decisions every week.

Matching Summary

Match Score: 75

** Amazon is seeking an Applied Scientist for its Demand Forecasting team to design and implement innovative deep learning architectures for large-scale time series forecasting. The role involves collaborating with software engineers to develop and deploy models that directly influence significant inventory decisions while contributing to the scientific community through research publications. **

Salary

142,800.00 - 193,200.00 USD annually

Skills & Requirements

Must-have

  • Foundation models for time series
  • Deep learning architectures
  • Transfer learning and zero-shot forecasting
  • Synthetic data generation techniques
  • Python and Scala for production code

Nice-to-have

  • Agentic GenAI workflows
  • Publishing research at top-tier conferences
  • Contributing to patent and science community

Key Requirements

  • Experience with Transformers, SSMs, or Graph Neural Networks
  • Experience with distributed training and inference pipelines
  • Experience with AI-coding assistants

Work Rights

Not specified

Tailored Resume

Cover Letter