Senior Applied Scientist, Agentic Workspaces

Amazon Amazon · Big Tech · Seattle, WA · Data Science

Senior Applied Scientist role focused on building predictive intelligence for capacity management in AWS workspaces. This involves developing ML systems for demand forecasting, resource optimization, and cost efficiency at enterprise scale. The role requires translating business needs into production ML systems, designing algorithms, and applying advanced ML techniques like time-series forecasting, reinforcement learning, and causal inference. Emphasis on low-latency, large-scale data processing, and collaboration with product and engineering teams.

What you'd actually do

  1. Architect and implement ML foundations for capacity management, building models that continuously learn and optimize across multiple dimensions including geography, platform, and instance type.
  2. Develop demand forecasting systems that anticipate usage patterns hours to weeks in advance, enabling proactive capacity decisions at scale.
  3. Build anomaly detection systems that identify capacity risks before they impact customers, improving service reliability and resilience.
  4. 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.
  5. 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.

Skills

Required

  • 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
  • Deep expertise in machine learning
  • 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

Nice to have

  • 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.

What the JD emphasized

  • production ML systems
  • low-latency requirements
  • large-scale datasets
  • time-series forecasting
  • reinforcement learning
  • causal inference

Other signals

  • production ML systems
  • ML foundations for capacity management
  • demand forecasting systems
  • anomaly detection systems
  • optimization algorithms
  • time-series forecasting
  • reinforcement learning
  • causal inference
  • large-scale datasets
  • low-latency requirements