Sr. Applied Science, Agentic WorkSpaces (AAWS)

Amazon Web Services (AWS); Seattle (WA); On-site; USD 167,000 - 226,000; Full time

Benefits offered by this job

Health insurance

RSUs

Paid time off

Parental leave

401(k) matching

Job summary

Amazon Development Center U.S., Inc. in Seattle is seeking a Senior Applied Scientist to own capacity modelling for Amazon WorkSpaces.

You will design, build, and continuously improve forecasting and optimization models to ensure the right compute, storage, and networking resources are available at the right time in the right regions at the lowest possible cost, without compromising the user experience. This is a high-impact, individual contributor role blending applied research with production

Qualifications

• PhD or Master’s + 6+ years of applied research experience.
• 3+ years of building ML models for business applications.
• Experience with Java, C++, Python or related languages.

Responsibilities

• Define and drive the scientific strategy for capacity modelling across WorkSpaces.
• Build advanced forecasting models for workspace usage across time horizons.
• Design optimization frameworks for resource placement, mix and pre-warming strategies.
• Develop causal and probabilistic models to understand demand drivers and uncertainty.
• Architect simulation systems for what-if analyses and capacity planning.
• Pioneer ML integration with operations research for joint demand prediction and resource allocation.
• Establish monitoring to measure forecast accuracy, capacity utilization and cost efficiency.
• Translate model outputs into actionable leadership recommendations.

Skills

Machine learning

Python

Java

C++

Applied research

Education

PhD

Master's degree + 6+ yrs experience

Tools

R

scikit-learn

Spark MLLib

MxNet

TensorFlow

NumPy

SciPy

Job description

Description

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset.

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset.
Within AAIS Amazon WorkSpaces is our cloud based virtual desktop service that delivers secure managed computing to over one million daily users across the globe enabling organizations to provision manage and scale desktops with the reliability and performance their workforce depends on.
We are looking for a Senior Applied Scientist to own and advance the science behind capacity modelling for Amazon WorkSpaces. 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

Amazon 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.
Amazon Web Services (AWS) is world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can achieve in the cloud.
Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empowers us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Basic Qualifications

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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 - 167,100.00 - 226,100.00 USD annually

Company - Amazon Development Center U.S., Inc.

Job ID: A10538043