Description

We're seeking a Sr. Quantitative UX Researcher who is passionate about shaping the future of AI-native business applications at scale. This is a rare opportunity to define the research strategy for one of Amazon's next generation of AI-powered solutions — influencing not just product direction, but the broader field of human-AI interaction.

As a Sr. Quantitative UX Researcher, you will own experimental design, quantitative analysis, insight generation, and the measurement strategies that turn AI capabilities into meaningful, trustworthy, and human-centered experiences for millions of customers worldwide. You will help teams build, test, and learn faster by turning ambiguous product questions into clear hypotheses, meaningful success metrics, rigorous studies, and decision-ready evidence. Your work will be instrumental to how we evaluate early ideas, improve production experiences, and decide where to invest next.

This is a quant-first role, with mixed-methods responsibilities as needed. We are looking for a researcher with deep expertise in experimentation, analytics, behavioral analysis, causal inference, and predictive intelligence. You will work in 0 to 1 environments, define success for new agentic AI products, and create new ways to measure experiences that do not yet have established playbooks. You will define success metrics for agentic AI products, identify the behavioral and attitudinal signals that matter most, and drive optimizations based on what the data shows. You will also invent AI-enabled tools, frameworks, and processes that improve how teams learn internally and scale research impact across the organization.


Key job responsibilities
- Lead quantitative and mixed-methods research for new and emerging AI products, especially in 0 to 1 spaces where customer needs, product behaviors, and success criteria are still being defined.
- Own experimental design from end to end. This includes defining hypotheses, selecting methods, identifying measures, designing studies, analyzing results, and translating findings into clear product actions.
- Pioneer AI-Native Research Innovation: Set the industry standard for AI-focused user research by inventing and adapting methodologies to understand how customers interact with AI systems, building trust, and driving adoption at scale
- Data Synthesis and Executive Storytelling: Triangulate research insights with engagement metrics, experiment data, user logs, and AI performance metrics to craft compelling, executive-ready narratives that drive product improvements and long-term business strategy
- Partner closely with product, design, applied science, and engineering to support a build, test, learn model of development, where evidence is used early and often to shape decisions.
- Establish Cross-Org Research Standards: Define and institutionalize research frameworks, measurement criteria, and reusable methodologies that create consistency and raise the bar across all AI solutions and teams within AWS


About the team
AWS Applied AI Solutions is an organization with ambitions to be a leading provider of business applications, leveraging Amazon's unique experience and expertise, used by millions of companies worldwide to manage day-to-day operations. Our mission is to accelerate our customers' businesses by delivering intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use.

The AWS Applied AI Solutions team is at the forefront of AI innovation, designing and building the next generation of intelligent business applications. We work closely with global Product, Engineering, Science, Marketing, and Business Development teams to deliver compelling AI-powered experiences that transform how businesses operate. Our research team plays a critical role in ensuring these AI solutions are not just technically advanced, but truly meet customer needs and drive meaningful business outcomes.