Applied Scientist: Predictive Capacity for Cloud WorkSpaces

Amazon Inc.; Seattle, Northern (WA, KY); Hybrid; USD 143,000 - 193,000; Full time

Benefits offered by this job

RSUs

401(k) matching

Parental leave

Health insurance

Adoption and Surrogacy coverage

Job summary

Amazon's AAIS group seeks an Applied Scientist to lead capacity modelling for WorkSpaces, building forecasting and optimization models that ensure scalable, cost-efficient resource provisioning across regions.

You will design production-ready solutions, leverage ML with operations research, and mentor teammates while shaping the scientific roadmap and collaboration with cross-functional teams.

Qualifications

• 3+ years of building models for business application experience.
• PhD, or Master’s degree and 4+ years of CS, CE, ML or related field experience.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.

Responsibilities

• Define and drive the scientific strategy for capacity modelling, establishing the research agenda that transforms how WorkSpaces forecasts demand, plans supply, and allocates resources across a globally distributed infrastructure.
• Build advanced demand forecasting models that predict workspace usage across multiple time horizons, from intraday spikes to long range growth trajectories, incorporating signals such as customer onboarding patterns, seasonal trends, regional expansion, and macroeconomic indicators.
• Design supply optimization frameworks that determine optimal resource placement, instance mix, and pre warming strategies, balancing availability, performance, and cost by reasoning over hardware constraints, pricing dynamics, and service level objectives.
• Develop causal and probabilistic models that move beyond trend extrapolation to true understanding of demand drivers, enabling the organization to distinguish organic growth from one time events, anticipate shifts in usage patterns, and quantify uncertainty in planning decisions.
• Architect simulation and scenario planning systems that allow business and engineering leaders to run what if analyses, stress test capacity plans against disruption scenarios, and evaluate trade offs between investment timing, risk tolerance, and customer experience.
• Pioneer the integration of machine learning with operations research, combining deep learning based forecasting with mathematical optimization to jointly solve the demand prediction and resource allocation problem in a way that neither discipline can achieve alone.
• Establish evaluation frameworks and monitoring systems that measure forecast accuracy, capacity utilization, and cost efficiency in production, creating tight feedback loops that drive continuous model improvement and build organizational trust in science driven planning.
• Influence the broader organization's capacity strategy by translating model outputs into actionable recommendations for leadership, identifying opportunities to extend capacity intelligence patterns to adjacent services, and mentoring scientists and engineers across the team.

Skills

Modeling experience

Java

C++

Python

Algorithms & data structures

Education

PhD

Master’s degree in CS/CE/ML

Tools

Unix/Linux

Job description

Amazon's AAIS group seeks an Applied Scientist to lead capacity modelling for WorkSpaces, building forecasting and optimization models that ensure scalable, cost-efficient resource provisioning across regions.

You will design production-ready solutions, leverage ML with operations research, and mentor teammates while shaping the scientific roadmap and collaboration with cross-functional teams.