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.

• 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.