Applied Scientist, Demand Forecasting

What does it take to build a foundation model that can forecast demand for hundreds of millions of products— including ones that have never been sold before? At Amazon, our Demand Forecasting team is tackling one of the most ambitious challenges in applied time series research: designing and building large‑scale foundation models that generalize across an enormous and diverse catalog of products, geographies, and business contexts.

Our team operates at a scale unmatched in industry or academia. You'll design experiments across millions of products simultaneously, develop new model architectures and training methodologies that push the boundaries of what foundation models can learn from vast, heterogeneous time series data. You'll explore techniques in transfer learning, zero‑shot forecasting, and synthetic data generation. The models you design will ship to production and directly influence hundreds of millions of dollars in automated inventory decisions every week.

Beyond operational impact, you'll publish your work at top‑tier conferences and contribute to advancing the state of the art in time series foundation models for the broader scientific community.

If you are a scientist who wants to work at the frontier of time series research, design novel solutions to problems no one else has solved at this scale, and see your research deployed to real‑world impact — this is the team for you.

Key Responsibilities

• Design and implement novel deep learning architectures (e.g., Transformers, SSMs, or Graph Neural Networks) for time‑series foundation models that generalize across hundreds of millions of products and diverse global contexts.
• Drive the full development cycle—from whiteboarding new algorithmic approaches to overseeing production‑scale deployments.
• Collaborate with SDEs to build high‑performance, distributed training and inference pipelines; translate complex scientific concepts into scalable, production‑grade code in Python and Scala.
• Leverage and develop agentic GenAI workflows to automate the end‑to‑end research cycle from synthesizing state‑of‑the‑art literature and auto‑generating experimental code to rapidly iterating on model architectures across millions of products.
• Maintain a high bar for scientific excellence by publishing novel research in top‑tier venues (e.g., NeurIPS, ICLR, KDD) and contributing to Amazon’s internal patent and science community.About the Team

The Demand Forecasting team sits at the heart of Amazon’s supply chain, building the science that determines what products are available, when, and at what cost for hundreds of millions of customers worldwide. Our mission is to push the frontier of large‑scale time series forecasting and to deploy that science where it creates real, measurable impact. We are a team of scientists who care deeply about both research rigor and real‑world outcomes: we don’t just publish—we ship.

Our work spans the full lifecycle—from foundational research and large‑scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.

Basic Qualifications

• PhD, or Master’s degree and 3+ years of deep learning, computer vision, human‑robotic interaction, or algorithms implementation experience.
• 3+ years of building models for business application experience.
• Experience programming in Java, C++, Python or related language.

Preferred Qualifications

• PhD in computer science, machine learning, engineering, or related fields.
• Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers.
• Experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets, or experience with training and deploying machine learning systems to solve large‑scale optimizations.
• Strong publication record in top‑tier AI/ML conferences (e.g., NeurIPS, ICLR, ICML, KDD, CVPR) or a history of contributing novel algorithmic improvements to production‑scale systems.
• Fluency in Python.

EEO Statement

Amazon is an equal‑opportunity employer and does not discriminate…