A
Not Specified Permanent

Bellevue, Washington · USA job

Applied Scientist, Agentic WorkSpaces (AAWS)

Amazon

Bellevue, Washington

Job description

Overview

In this role you will lead capacity intelligence for Amazon WorkSpaces, turning forecasting into a predictive, self-optimizing engine. You'll define the scientific strategy, build advanced demand forecasts, and design supply optimization to balance cost, performance, and availability. You will work at the intersection of research and production systems, delivering measurable impact at scale. This is a high-impact, IC position that pairs machine learning with operations research to shape capacity planning across a global infrastructure.

Compensation / Benefits
  • health insurance (medical, dental, vision)
  • RSUs and sign-on bonuses
  • 401(k) matching
  • paid time off
  • parential leave
  • flexible work culture
Responsibilities
  • Define and drive the scientific strategy for capacity modelling and forecasting
  • Build advanced demand forecasting models across intraday to long-range horizons
  • Design supply optimization frameworks for resource placement, instance mix, and pre-warming
  • Develop causal and probabilistic demand models to quantify drivers and uncertainty
  • Architect simulation and scenario planning systems for what-if analyses and risk assessment
  • Pioneer integration of machine learning with operations research for joint demand prediction and resource allocation
  • Establish evaluation and monitoring to drive continuous model improvement and trust in planning
  • Influence capacity strategy with actionable recommendations and mentorship across the team
Key requirements
  • 3+ years of experience building models for business applications
  • PhD, or Master's degree in CS, CE, ML or related field with 4+ years of experience
  • Experience with patents or publications at top-tier conferences or journals
  • Programming experience in Java, C++, Python or related language
  • Experience in algorithms & data structures, parsing, numerical optimization, data mining, parallel and distributed computing, HPC
  • cross-functional collaboration
  • mentoring scientists and engineers
  • curiosity and adaptability
  • machine learning
  • forecasting
  • operations research

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