Quantitative UX Researcher, Applied AI Solutions

📍 Job Overview

Job Title: Quantitative UX Researcher, Applied AI Solutions

Company: Amazon

Location: Sunnyvale, California; Seattle, Washington; Arlington, Virginia; New York, New York; Herndon, Virginia (United States)

Job Type: Full-Time

Category: User Experience Research / Applied AI

Date Posted: 2026-08-17

Experience Level: Mid-Level (2-5 years)

Remote Status: On-site

🚀 Role Summary

• Lead quantitative and mixed-methods research initiatives for cutting-edge Applied AI solutions, influencing product strategy and roadmap.

• Design and execute sophisticated research studies, including experimentation, behavioral analysis, and causal inference, to drive product adoption and customer value.

• Define, measure, and analyze key success metrics for AI-powered features, focusing on adoption, trust, usability, and comprehension.

• Collaborate closely with Product, Design, and Engineering teams in a fast-paced, Agile environment to foster a build-test-learn culture.

• Translate complex product questions into testable hypotheses and actionable insights that inform critical product decisions throughout the lifecycle.

📝 Enhancement Note: While the role title is "Quantitative UX Researcher," the description heavily emphasizes applied AI and business applications, suggesting a strong focus on driving business outcomes and product adoption rather than purely academic UX research. The "quant-first" approach with mixed-methods as needed indicates a need for deep statistical and experimental design skills.

📈 Primary Responsibilities

• Spearhead the end-to-end execution of quantitative and mixed-methods UX research for AI-powered business applications, from initial concept validation to iterative improvements.

• Develop robust study designs, define clear hypotheses, select appropriate methodologies, identify key behavioral and attitudinal measures, and analyze results to drive actionable product recommendations.

• Design, implement, and analyze experiments (A/B tests, multivariate tests), surveys, and behavioral data analyses to deeply understand customer needs and product performance.

• Establish and continuously monitor critical success metrics related to adoption, trust, usability, comprehension, and overall customer value for AI-driven features.

• Synthesize findings from diverse sources, including primary research, product usage data, and customer feedback, to generate clear, concise, and impactful insights for product teams.

• Actively partner with Product Managers, Designers, and Engineers to embed a data-driven, evidence-based approach into the product development lifecycle, facilitating rapid iteration and learning.

📝 Enhancement Note: The responsibilities highlight a proactive role in defining research strategy and metrics for new AI products, indicating a need for researchers comfortable with ambiguity and early-stage product development where success metrics may not be pre-defined.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Human-Computer Interaction (HCI), Human Factors, Cognitive Psychology, Computer Science, Statistics, or a related quantitative field.

• Master's degree in Human Factors, HCI, Cognitive Psychology, or a related field is strongly preferred, indicating a desire for advanced theoretical and practical knowledge. Experience:

• Minimum of 3 years of progressive experience in leading User Research projects with a demonstrable track record of impactful contributions.

• Proven experience in conducting end-to-end research, encompassing study design, participant recruitment, moderation, rigorous analysis, and comprehensive reporting.

• Hands-on experience with a variety of research methodologies, including lab-based and remote usability testing, iterative prototype testing, survey design, and the application of multiple methods within a single study. Required Skills:

• Expertise in quantitative research methodologies, including experimental design, survey design, and statistical analysis.

• Strong proficiency in defining and analyzing product metrics to measure adoption, engagement, usability, and customer value.

• Demonstrated ability in causal inference and behavioral analysis to understand user decision-making and product impact.

• Experience in designing and executing A/B tests and other controlled experiments.

• Excellent communication and presentation skills, with the ability to articulate complex findings and recommendations clearly to diverse stakeholders.

• Familiarity with user research platforms and tools for data collection and analysis. Preferred Skills:

• Knowledge of advanced statistical techniques and their application in user research.

• Experience conducting research in a global context, understanding cultural nuances and diverse user needs.

• Experience working within highly Agile/Scrum development environments, adapting research to rapid iteration cycles.

• Familiarity with AI/ML concepts and their application in user-facing products.

• Experience with mixed-methods research, integrating qualitative insights to complement quantitative findings.

📝 Enhancement Note: The emphasis on "quantitative first" and "causal inference" suggests that candidates with a strong statistical or econometrics background will be highly competitive. The requirement for a portfolio that demonstrates impact is critical.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• A comprehensive portfolio showcasing a minimum of 3 years of work experience, with a clear emphasis on quantitative research projects and their demonstrable impact.

• Deliverables should clearly illustrate the entire research process, including study plans, methodology justifications, detailed analysis reports, and actionable recommendations.

• Examples should highlight the candidate's ability to define and measure success metrics for product features, particularly in the context of AI or complex technological solutions.

• Showcase how research findings directly influenced product decisions, design iterations, or strategic roadmap adjustments, ideally with quantitative evidence of the impact. Process Documentation:

• Evidence of designing and executing rigorous research studies, including detailed methodology documentation, participant criteria, and data collection protocols.

• Samples of analytical approaches used, demonstrating the ability to derive meaningful insights from quantitative data, including experimental results and behavioral metrics.

• Examples of how research findings were translated into clear, concise, and actionable product recommendations for cross-functional teams.

• Documentation of experience in defining and tracking key performance indicators (KPIs) and success metrics for product features, especially in early-stage or evolving product areas.

📝 Enhancement Note: Candidates should prepare to walk through their portfolio, explaining the context, their specific role, the methodologies used, the analytical approach, and the measurable impact of their research. Focus on how they defined success and measured it quantitatively.

💵 Compensation & Benefits

Salary Range:

• Sunnyvale, California & New York, New York: $143,100 - $193,600 USD annually

• Arlington, Virginia & Herndon, Virginia: $130,100 - $176,000 USD annually

• Seattle, Washington: $130,100 - $176,000 USD annually

📝 Enhancement Note: The salary ranges provided are specific to the listed locations and reflect mid-level quantitative research roles within a major tech company. These ranges are competitive for the respective U.S. markets. The total compensation package will also include sign-on bonuses and Restricted Stock Units (RSUs), which are standard for Amazon roles at this level.

Benefits:

• Comprehensive health insurance package: medical, dental, and vision coverage.

• Prescription drug coverage.

• Basic Life & Accidental Death & Dismemberment (AD&D) insurance, with options for supplemental life plans.

• Employee Assistance Program (EAP) for confidential support.

• Mental health support services.

• Medical Advice Line for health-related queries.

• Flexible Spending Accounts (FSAs) for healthcare and dependent care expenses.

• Adoption and surrogacy reimbursement assistance.

• 401(k) retirement savings plan with company matching.

• Generous paid time off (PTO).

• Parental leave benefits.

• Restricted Stock Units (RSUs) as part of the overall compensation package. Working Hours:

• Standard full-time hours, typically around 40 hours per week.

• While the role is on-site, Amazon often offers some flexibility in daily schedules, though core collaboration hours will apply.

📝 Enhancement Note: The benefits package is extensive and typical for a large technology employer like Amazon, covering a wide range of employee well-being and financial security needs. The inclusion of RSU's is a significant component of total compensation.

🎯 Team & Company Context

🏢 Company Culture

Industry: E-commerce, Cloud Computing, Artificial Intelligence, Digital Streaming, Consumer Electronics. Amazon operates across multiple diverse industries, with this role specifically within AWS Applied AI Solutions, focusing on business applications.

Company Size: Extremely Large (1,000+ employees, likely over 1 million globally). This scale means established processes, significant resources, and opportunities for impact across vast user bases.

Founded: 1994. Amazon has a long history of innovation and a culture that encourages pioneering new technologies and business models.

Team Structure:

• The AWS Applied AI Solutions research team is likely a specialized group within a larger product organization, focusing on user experience for AI-driven business tools.

• Reporting structure will likely involve a research manager or lead, with close collaboration with product managers, designers, and engineering teams (likely Agile pods).

• Cross-functional collaboration is a cornerstone, with researchers acting as key partners in the product development lifecycle, bridging user needs with technical capabilities. Methodology:

• Data Analysis & Insights: Emphasis on rigorous quantitative analysis, experimentation, and causal inference to derive objective insights.

• Workflow Planning & Optimization: Application of Agile methodologies, with research integrated into rapid build-test-learn cycles to optimize product features and user flows.

• Automation & Efficiency: Leveraging AI and automation within products, and employing efficient research practices to deliver timely insights in a fast-paced environment.

Company Website: https://www.amazon.com and https://aws.amazon.com/

📝 Enhancement Note: Amazon's culture is known for its customer obsession, bias for action, and high standards. For researchers, this translates to a need for data-driven decision-making, comfort with ambiguity, and a focus on delivering measurable customer value. The "Applied AI Solutions" context implies a strong business-oriented approach to research.

📈 Career & Growth Analysis

Operations Career Level: Mid-Level Researcher (2-5 years experience). This role is positioned to contribute significantly to product strategy and execution, moving beyond basic task execution to lead independent research initiatives.

Reporting Structure: Typically reports to a Research Manager or Lead within the AWS Applied AI Solutions organization. Will work closely with cross-functional teams including Product Managers, UX Designers, and Software Engineers.

Operations Impact: This role has a direct impact on the success of AI-powered business applications by ensuring they are usable, trustworthy, and valuable to customers. Research insights will shape product direction, feature development, and ultimately, customer adoption and satisfaction, contributing to revenue and market share for AWS services.

Growth Opportunities:

• Operations Skill Advancement: Deepen expertise in quantitative research methodologies, experimental design, causal inference, and AI-specific user research challenges. Opportunities to learn and apply new statistical techniques and research tools.

• Leadership Development: Potential to mentor junior researchers, lead larger or more complex research initiatives, and influence research strategy for the Applied AI Solutions domain.

• Specialization: Develop deep expertise in AI-UX, business applications, or specific AWS product areas, becoming a go-to subject matter expert. Opportunities to move into senior researcher or principal researcher roles.

📝 Enhancement Note: Amazon provides clear paths for career growth within its research functions. This role offers a strong foundation for a career focused on the intersection of AI, user experience, and business impact, particularly within the cloud computing domain.

🌐 Work Environment

Office Type: On-site. The role requires regular presence in one of Amazon's major tech hubs.

Office Location(s): Sunnyvale, CA; Seattle, WA; Arlington, VA; New York, NY; Herndon, VA. These are major technology centers with robust infrastructure and talent pools.

Workspace Context:

• Collaborative Environment: Expect a dynamic, fast-paced work environment where collaboration with diverse teams (Product, Design, Engineering, Science) is constant and essential.

• Tools and Technology: Access to state-of-the-art research tools, robust data infrastructure, and cutting-edge AI/ML platforms. The environment encourages leveraging data and technology to drive insights.

• Team Interaction: Frequent opportunities for team meetings, brainstorming sessions, design reviews, and knowledge-sharing initiatives within the UX research community at Amazon.

Work Schedule: Standard full-time hours, typically 40 hours per week. While on-site, there's an expectation of flexibility to meet project deadlines and collaborate effectively across time zones if needed for global research, though core hours will be centered around the primary office location.

📝 Enhancement Note: The on-site requirement suggests a preference for in-person collaboration and team cohesion, which is common for roles involving early-stage product development and complex technical domains like Applied AI.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Recruiter screen to assess basic qualifications, experience, and interest.

• Hiring Manager Interview: Discussion focused on experience, motivations, and fit with the team's goals.

• Portfolio Review & Deep Dive: A critical stage where candidates present 1-2 impactful research projects from their portfolio. This will involve detailed discussion on study design, methodology, analysis, insights, and the impact of their work. Expect in-depth questions about quantitative approaches, experimental design, and causal inference.

• Technical Interviews: May include case studies or problem-solving exercises focused on quantitative analysis, experimental design for AI products, or interpreting complex data sets.

• Team/Cross-functional Interviews: Interactions with potential peers (other researchers) and key collaborators (Product Managers, Designers, Engineers) to assess collaboration style and domain understanding.

• Bar Raiser Interview: A final interview focused on Amazon's Leadership Principles and ensuring the candidate meets Amazon's high bar for talent.

Portfolio Review Tips:

• Focus on Impact: Select projects that clearly demonstrate how your research led to tangible product improvements or business outcomes. Quantify impact whenever possible.

• Highlight Quantitative Rigor: For this role, emphasize projects where you used experimental design, statistical analysis, and causal inference. Explain your choices of methods and metrics.

• Structure Your Narrative: For each project, clearly articulate the problem, your approach, your specific contributions, the key findings, and the resulting actions/impact. Use a structure like STAR (Situation, Task, Action, Result).

• Be Prepared for Deep Dives: Anticipate detailed questions about your methodology, analysis, limitations of your studies, and alternative approaches you considered.

• Showcase AI Relevance: If possible, include projects that touched on AI, machine learning, or complex technological systems.

Challenge Preparation:

• Quantitative Problem Solving: Practice designing experiments for AI features, defining success metrics for ambiguous products, and analyzing simulated user data.

• Causal Inference Scenarios: Be ready to discuss how you would establish causality in user behavior related to AI features.

• Stakeholder Communication: Prepare to explain complex quantitative findings and their implications in a clear, concise manner to non-technical audiences.

📝 Enhancement Note: The portfolio review is paramount. Candidates should meticulously prepare presentations that showcase their quantitative prowess and ability to drive product decisions with data. Understanding Amazon's Leadership Principles is also crucial for later stages.

🛠 Tools & Technology Stack

Primary Tools:

• Experimentation Platforms: Experience with A/B testing tools (e.g., internal Amazon tools, Optimizely, VWO) for designing and analyzing experiments.

• Survey Tools: Proficiency with survey platforms (e.g., Qualtrics, SurveyMonkey, internal Amazon tools) for designing and deploying quantitative surveys.

• Statistical Software: Strong command of statistical analysis software such as R, Python (with libraries like SciPy, StatsModels, Pandas), or SPSS for data manipulation and analysis.

• Data Analysis & Visualization: Experience with tools for data analysis and visualization like Tableau, Power BI, or advanced spreadsheet functions.

Analytics & Reporting:

• Product Analytics Platforms: Familiarity with product analytics tools (e.g., Amplitude, Mixpanel, Google Analytics, internal Amazon tools) for tracking user behavior and product usage.

• Data Warehousing/Querying: Experience with SQL for querying large datasets from data warehouses (e.g., Redshift, Snowflake, BigQuery).

• Dashboarding Tools: Ability to create clear and compelling dashboards to communicate key metrics and research findings.

CRM & Automation:

• While not the primary focus, understanding how user research data integrates with CRM systems (e.g., Salesforce) or marketing automation platforms can be beneficial for context.

• Familiarity with project management and collaboration tools common in Agile environments (e.g., Jira, Confluence).

📝 Enhancement Note: The emphasis is on analytical rigor and data manipulation. Proficiency in statistical programming languages (R, Python) and SQL is highly desirable given the quantitative nature of the role.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: Deeply understanding and advocating for customer needs and value, ensuring AI solutions genuinely solve problems and enhance user experience.

• Bias for Action: Proactively identifying research opportunities and driving studies forward to deliver timely insights, even in ambiguous situations.

• Invent and Simplify: Developing innovative research approaches and tools to tackle complex AI UX challenges efficiently.

• High Standards: Maintaining rigorous methodological standards in research design, analysis, and reporting to ensure the quality and credibility of insights.

• Dive Deep: Thoroughly analyzing data, exploring root causes, and ensuring a comprehensive understanding of user behavior and product performance.

Collaboration Style:

• Cross-functional Integration: Actively embedded within product teams, working hand-in-hand with Product Managers, Designers, and Engineers to ensure research is integrated throughout the product lifecycle.

• Data-Driven Communication: Presenting findings persuasively with data, fostering a culture where decisions are informed by evidence.

• Constructive Feedback: Engaging in open and honest feedback loops with team members to continuously improve research practices and product outcomes.

📝 Enhancement Note: Amazon's Leadership Principles are the bedrock of its culture. Candidates who can articulate how their work aligns with these principles will resonate strongly. For this role, Customer Obsession, Bias for Action, and Dive Deep are particularly relevant.

⚡ Challenges & Growth Opportunities

Challenges:

• Defining Success for AI: Navigating the ambiguity of defining and measuring success for novel AI-powered features where traditional UX metrics may not directly apply. This requires creativity in metric definition and experimental design.

• Rapid Iteration Cycles: Adapting research methodologies and timelines to keep pace with the rapid development cycles common in Agile/Scrum environments, particularly for cutting-edge AI products.

• Data Complexity: Working with large, complex datasets and integrating various data sources (behavioral, survey, qualitative) to form a holistic understanding of user interaction with AI.

• Ethical Considerations in AI: Addressing user trust, transparency, and potential biases within AI systems through research, ensuring responsible product development.

Learning & Development Opportunities:

• AI/ML User Experience: Gaining deep expertise in the unique UX challenges and opportunities presented by artificial intelligence and machine learning technologies.

• Advanced Quantitative Methods: Opportunities to refine skills in advanced statistical modeling, causal inference, experimental design, and predictive analytics.

• Cross-Disciplinary Learning: Collaborating closely with AI scientists, engineers, and product leaders to expand understanding of AI capabilities and business applications.

• Industry Conferences & Publications: Potential to attend leading UX and AI conferences, and opportunities to contribute to internal or external publications.

📝 Enhancement Note: This role offers the chance to be at the forefront of AI-UX research, tackling some of the most complex and exciting challenges in the field. The growth opportunities are substantial for individuals passionate about shaping the future of intelligent applications.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you designed an experiment to understand user behavior with a complex technological product. What were your hypotheses, how did you measure success, and what was the outcome?" (Focus on quantitative rigor, hypothesis testing, and impact.)

• "How would you approach defining success metrics for a new AI feature that aims to automate a complex business task, considering user adoption, trust, and efficiency?" (Assess ability to handle ambiguity, define metrics, and consider AI-specific factors.)

• "Walk me through a research project where you had to integrate quantitative and qualitative data. What were the challenges, and how did you synthesize the findings to provide actionable recommendations?" (Demonstrate mixed-methods capability and synthesis skills.) Company & Culture Questions:

• "How does your research approach align with Amazon's Leadership Principles, particularly Customer Obsession and Bias for Action?" (Prepare specific examples linking your work to these principles.)

• "Describe a time you had to influence product direction with data when stakeholders had differing opinions. How did you present your findings and build consensus?" (Assess communication, persuasion, and data-driven influence.)

• "How do you stay current with advancements in AI and their implications for user experience research?" (Showcase continuous learning and domain interest.) Portfolio Presentation Strategy:

• Structure for Impact: For each project, clearly outline the business/product context, the research question, your specific role and methodology, the key quantitative findings, and the resulting product or business impact. Use data visualizations effectively.

• Quantify Everything: Emphasize the numbers – sample sizes, statistical significance, percentage changes in metrics, ROI if applicable. Explain why the quantitative results were compelling.

• Anticipate Technical Deep Dives: Be ready to explain the statistical assumptions behind your analyses, potential confounds, and limitations of your studies. Demonstrate a deep understanding of your chosen methods.

• Focus on Actionability: Clearly articulate how your insights translated into concrete product changes or strategic decisions.

📝 Enhancement Note: The interview process is designed to rigorously assess both technical research skills and alignment with Amazon's culture. Candidates must be prepared to articulate their quantitative expertise and demonstrate a data-driven mindset with a strong customer focus.

📌 Application Steps

To apply for this Quantitative UX Researcher position:

• Submit your application through the Amazon Jobs portal.

• Customize Your Resume: Highlight specific projects and skills related to quantitative research, experimental design, causal inference, AI/ML applications, and data analysis. Use keywords from the job description.

• Prepare Your Portfolio: Curate 1-2 strong case studies that exemplify your quantitative research expertise, impact, and ability to work with complex products, especially AI-related ones. Ensure clear documentation of methodology, analysis, and outcomes.

• Practice Your Presentation: Rehearse presenting your portfolio case studies, focusing on clear articulation of quantitative findings, impact, and alignment with Amazon's Leadership Principles.

• Research Amazon's AI Strategy: Familiarize yourself with AWS Applied AI Solutions and Amazon's broader AI initiatives to demonstrate genuine interest and understanding of the domain.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

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