Job Description
Description About the Organization AWS is on a mission to transform how businesses operate by delivering intelligent, cloud-powered applications. Our Applied AI Solutions organization accelerates customer success through intuitive, differentiated technology that solves enduring business challenges — blending vision with real-world expertise to build turnkey solutions that are easy to adopt and built to scale. Within this organization, we are building the next generation of secure, intelligent workspaces — environments purpose-built for human-AI collaboration at enterprise scale. The Role We are looking for a Senior Applied Scientist to build the predictive intelligence powering capacity management for our workspace platform — developing machine learning systems that forecast demand, optimize resource allocation, and enable cost-efficient scaling at massive scale. This role requires someone who can translate complex business requirements into production ML systems, designing algorithms that balance customer experience with operational efficiency across a large and diverse fleet of capacity pools. What You'll Do Architect and implement ML foundations for capacity management, building models that continuously learn and optimize across multiple dimensions including geography, platform, and instance type. Develop demand forecasting systems that anticipate usage patterns hours to weeks in advance, enabling proactive capacity decisions at scale. Build anomaly detection systems that identify capacity risks before they impact customers, improving service reliability and resilience. Design optimization algorithms that make high-frequency, automated decisions balancing two critical forces: ensuring a flawless customer experience where every operation succeeds, while maximizing cost efficiency through intelligent resource utilization and placement strategies. Apply advanced ML techniques including time-series forecasting, reinforcement learning, and causal inference to measure the true impact of capacity decisions on customer experience and cost. Engineer features from large-scale datasets spanning usage signals, session patterns, and infrastructure telemetry — capturing complex interactions across diverse workload types. Partner closely with product and engineering teams to translate product vision into scientific solutions, deploying models that process millions of predictions daily with sub-second latency requirements. What Success Looks Like ML systems that enable the service to remain profitable while capacity-related customer impacts become increasingly rare. Measurable business impact through reduced capacity waste, improved cost efficiency, and elimination of customer-impacting capacity events. Scientific innovation that unlocks significant cost savings through predictive resource commitment strategies and intelligent automated decision-making. Models that maintain the safety margins needed to absorb demand volatility without customer impact. An ML foundation that enables distributed, autonomous decision-making while maintaining consistent quality at scale. What We're Looking For Deep expertise in machine learning, with hands-on experience building and deploying production ML systems. Strong background in time-series forecasting and handling demand volatility across diverse workload patterns. Experience with reinforcement learning for dynamic resource allocation and causal inference for impact measurement. Ability to work with large-scale datasets and engineer features that capture complex, multi-dimensional interactions. Strong systems thinking — able to design end-to-end ML pipelines that operate reliably at scale with low-latency requirements. Excellent collaboration skills — comfortable partnering with product managers, engineers, and business stakeholders to drive scientific solutions from concept to production. A track record of measurable business impact through applied ML research and deployment. Key job responsibilities 1/ Work independently on ambiguous problems: Independently work on capacity forecasting problems that are not well defined or structured, identifying and framing new research challenges associated with broad problem areas, delivering with limited guidance. 2/ Influence across multiple teams: Drive alignment on ML approaches and capacity strategies across product, engineering, and operations teams. Actively mentor and develop others on the team. 3/ Deliver end-to-end production solutions: Develop and deliver complete solutions including scientific contributions that are deployed in production. Make technical trade-offs balancing long-term invention with short-term delivery Lead on medium-to-large business problems