Senior Applied Scientist, Applied AI Solutions GTM

About the role

AWS Applied AI Solutions (AAIS) builds and deploys production AI solutions that drive measurable business outcomes at scale. The GTM Acceleration team within AAIS activates the field, measures impact, and scales what works. We bridge the gap between AAIS product and science teams and the worldwide field organization, ensuring our AI solutions reach customers effectively, quantify the value we deliver, and build repeatable motions that scale globally.

Responsibilities

• Design, develop, and deploy statistical models and machine learning pipelines to drive product improvements, business decisions, and customer outcomes
• Work directly with customers during production pilots to build and deploy AI solutions that demonstrate measurable business value
• Design and execute A/B experiments and causal inference analyses to measure the impact of new features and model changes
• Create ROI models, business case tools, and forecasting systems for demand prediction, capacity planning, workforce optimization, and value quantification
• Apply NLP and generative AI techniques to extract insights from structured and unstructured data at scale, and partner with software engineers to productionize models with reliability, monitoring, and operational excellence
• Build and own customer analytics capabilities including segmentation, usage trend analysis, propensity modeling, and foundational datasets combining service usage with sales data
• Create self-service analytics platforms and automated insight delivery mechanisms that enable leadership to pull strategic intelligence on demand
• Enable field teams with reusable analytical assets, diagnostic notebooks, benchmarking studies, and scalable tooling that accelerate customer engagements
• Own success metrics and create mechanisms to measure model performance, adoption, and business impact across customer cohorts
• Define strategic frameworks and GTM recommendations by segment, translating data patterns and market signals into actionable go-to-market motions and investment priorities
• Communicate findings and technical trade-offs to senior leadership and customer executives through written documents (6-pagers, science reviews) and presentations, operating as a shared resource across 2-3 teams simultaneously

Qualifications

• PhD, or Master's degree and 6+ years of applied research experience
• 5+ years of building machine learning models for business application experience
• Experience with neural deep learning methods and machine learning
• Experience managing analytics, data science or technology teams, with a product or insight focus
• Experience working with diverse or differing data sets including creating and compiling data into a final distribution for management consumption
• Experience with customer segmentation, profiling, and targeting