We are looking for an Applied Scientist to be part of the tiger team building the capacity modelling for Amazon WorkSpaces, owning and advancing the science behind it. You will design, build, and continuously improve the forecasting and optimization models that ensure the right compute, storage, and networking resources are available at the right time, in the right regions, at the lowest possible cost, without ever compromising the end user experience.

This is a high impact individual contributor role for someone who thrives at the intersection of applied research and production systems. You will define the scientific roadmap for capacity intelligence, turning reactive provisioning into a predictive, self optimizing engine that anticipates demand before customers feel any constraint.

Key job 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.

About the team

As part of the AWS solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. we blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use.

Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

BASIC 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 any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

PREFERRED QUALIFICATIONS

- Experience using Unix/Linux

- Experience in professional software development

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually