Amazon Web Services (AWS) Applied AI Solutions (AAIS) is on a mission to make AI real for enterprises. We build and deploy production AI solutions that drive measurable business outcomes at scale, bringing together applied scientists, AI architects, business development professionals, and GTM specialists to help customers move from AI experimentation to production impact.
Within AAIS, the GTM Acceleration team activates the field, measures impact, and scales what works. We are the connective tissue between AAIS product and science teams and the worldwide field organization, ensuring our AI solutions reach customers effectively, that we quantify the value we deliver, and that we build repeatable motions that scale globally.
We are looking for an Applied Scientist who will serve as a force multiplier across our customer engagement teams, building the analytical foundations, predictive models, and reusable tooling that power our go-to-market strategy. You will work at the intersection of data science, machine learning, and business strategy, building models that quantify our value proposition, and creating scalable analytical assets that accelerate every engagement. This is a highly visible, high-impact role where your work directly influences how we demonstrate and measure the value of AWS AI solutions for enterprise customers.
You will operate with significant autonomy, owning the scientific direction of your projects while collaborating with software engineers, product managers, and business stakeholders. You will identify the right methodology for each problem, whether that is a classical statistical approach, a modern deep learning technique, or a novel combination, and communicate your findings clearly to both technical and non-technical audiences. This role spans Connect Customer initiatives and across the Applied AI solution portfolio, offering the opportunity to pioneer data science approaches that scale intelligent analytics worldwide.
If you thrive at the intersection of rigorous science and customer-facing impact and are energized by translating complex model outputs into business decisions, we want to talk to you.
Key job 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
Build 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 (by size tier, AI adoption, product penetration, entitlement), 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
About the team
Diverse Experiences
AWS 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.
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