Applied Scientist, Demand Forecasting

Amazon • Bellevue, WA

2d

About The Position

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. This is not incremental modeling work. We are redefining what's possible in demand forecasting through novel architectures, training strategies, and data generation techniques.

Our team operates at a scale that is unmatched in industry or academia. You'll design experiments across millions of products simultaneously, developing 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 here 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.

Requirements

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

Nice To Haves

• 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.

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.

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree