Sr. UX Researcher, Applied AI Solutions

📍 Job Overview

Job Title: Sr. UX Researcher, Applied AI Solutions

Company: Amazon

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

Job Type: Full-time

Category: User Experience Research / Applied AI

Date Posted: 2026-09-15

Experience Level: 5-10 Years

Remote Status: On-site

🚀 Role Summary

• Lead comprehensive, end-to-end user research programs for AI-native business applications within AWS, driving product strategy and de-risking development cycles.

• Conduct a diverse range of research methodologies, from rapid evaluative studies to foundational qualitative and quantitative investigations, to uncover unmet customer needs and adoption barriers.

• Collaborate closely with senior cross-functional stakeholders (Product, Design, Engineering, Applied Science) to translate complex research findings into actionable insights that influence roadmaps and customer outcomes.

• Shape the research approach for ambiguous problem spaces, leveraging strong methodological judgment to ensure credible and impactful recommendations that enhance usability, adoption, and trust in AI solutions.

📝 Enhancement Note: This role is positioned as a senior individual contributor with significant autonomy, expected to lead complex research initiatives and mentor junior team members. The focus on "Applied AI Solutions" within AWS indicates a need for researchers who can navigate technically complex domains and understand the unique challenges of AI product development and adoption. The emphasis on reducing ambiguity and de-risking investments highlights the strategic importance of this role in the product lifecycle.

📈 Primary Responsibilities

• Design, execute, and communicate findings from end-to-end research programs across a dedicated product area, ensuring a deep understanding of customer workflows and AI solution adoption.

• Employ a broad spectrum of research methods, including field research, ethnography, lab-based and remote user testing, concept testing, diary studies, journey mapping, surveys, and mixed-methods programs.

• Partner with senior leaders in Product Management, Design, Engineering, and Applied Science to inform product strategy, evaluate new concepts and prototypes, and iteratively improve live customer experiences.

• Define research plans for complex and ambiguous problem spaces, applying robust methodological judgment to select the most appropriate research approach based on the decision at hand, product stage, and level of risk.

• Proactively identify unmet customer needs, product gaps, adoption barriers, and trust issues early in the development process to help teams reduce ambiguity and de-risk strategic investments.

• Translate research findings into clear, concise, and actionable recommendations that directly improve usability, adoption, trust, and the overall quality of the customer experience for AI-powered business applications.

• Influence product roadmaps and prioritization by ensuring customer evidence is visible, credible, and actionable at key decision-making junctures.

• Develop deep domain expertise by continuously studying customer workflows, prototypes, competitive products, and emerging industry trends in AI and business applications.

• Mentor junior researchers, providing guidance and support to raise the overall quality, consistency, and impact of research practices within the team.

• Explore and implement thoughtful applications of AI within the research and product development process itself to enhance speed, depth, or quality of insights.

📝 Enhancement Note: The responsibilities emphasize a blend of tactical execution and strategic influence. The expectation to "define research plans for ambiguous problem spaces" and "influence roadmaps and prioritization" points to a role that requires proactive problem-solving and strong communication skills to advocate for the customer. The inclusion of "mentoring junior researchers" and "exploring AI in the research process" suggests opportunities for leadership and innovation.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Human-Computer Interaction Design (HCDE), Human Factors, Cognitive Psychology, or a closely related field.

• Master's degree in Human Factors, HCDE, Cognitive Psychology, or a related field is strongly preferred, indicating a need for advanced theoretical understanding and practical application. Experience:

• Minimum of 5 years of proven success leading User Research projects with demonstrable impact on product development and strategy.

• Extensive hands-on experience with a wide array of research methodologies, including but not limited to: field research, ethnography, lab-based user testing, remote testing, paper prototype testing, iterative prototype testing, concept testing, and survey design.

• Comprehensive experience with all facets of the research lifecycle: study design, participant recruitment, moderation, data analysis, synthesis, and reporting of findings.

• Proficiency in behavioral data collection, quantitative data analysis, and statistical interpretation to support robust research conclusions. Required Skills:

• User Research Leadership: Proven ability to lead end-to-end research programs from conception to actionable insights, with a strong track record of influencing product decisions.

• Methodological Expertise: Deep understanding and practical application of diverse qualitative and quantitative research methods (ethnography, usability testing, surveys, interviews, journey mapping, etc.).

• Analytical & Synthesis Skills: Ability to analyze complex behavioral and quantitative data, synthesize findings into clear themes, and translate them into impactful recommendations.

• Cross-Functional Collaboration: Demonstrated success in partnering effectively with Product Management, Design, Engineering, and Applied Science teams to integrate research into the product development lifecycle.

• Communication & Storytelling: Excellent verbal and written communication skills, with the ability to articulate complex findings and their implications to diverse stakeholders, including senior leadership.

Preferred Skills:

• Experience in a technical field such as software development, network development, IT, or related areas, to better understand the context of applied AI solutions.

• Experience conducting UX Research in non-US markets, understanding cultural nuances and their impact on user behavior and AI adoption.

• Experience working within highly Agile/Scrum development environments, adapting research practices to fast-paced, iterative product cycles.

• Familiarity with AI concepts and their application in business solutions, enabling more insightful research design and analysis.

📝 Enhancement Note: The "Basic Qualifications" are quite robust, requiring significant direct experience across multiple research methodologies and the entire research lifecycle. The "Preferred Qualifications" suggest a strong advantage for candidates with technical backgrounds or international research experience, particularly relevant for a global company like Amazon operating in complex technical domains like AI. The emphasis on a portfolio is critical for demonstrating practical application of these skills.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrated Impact: Showcase case studies that clearly articulate a research problem, the methodology employed, the research process, and, most importantly, the measurable impact of your insights on product decisions, user adoption, or business outcomes.

• Methodological Breadth: Include examples of research across a variety of methodologies (e.g., ethnographic studies, usability tests, concept evaluations, surveys) to demonstrate versatility and appropriate application of techniques.

• Deliverable Variety: Present a range of deliverables, such as detailed study plans, comprehensive research reports, personas, journey maps, and executive summaries, illustrating your ability to tailor outputs to different audiences.

• Problem Framing: Highlight instances where you identified and framed complex, ambiguous research problems, and how your approach led to clarity and actionable solutions.

• AI/Technical Context: If possible, include examples of research conducted within technical or AI-related domains, demonstrating an understanding of the unique user needs and challenges in these areas.

Process Documentation:

• Research Planning: Provide examples of how you develop detailed research plans, including defining objectives, research questions, target participant profiles, recruitment strategies, and timelines.

• Execution Protocols: Showcase your approach to conducting research ethically and efficiently, including moderation guides, survey instruments, and data collection protocols.

• Analysis & Synthesis Frameworks: Detail your methods for analyzing qualitative and quantitative data, identifying key themes, and synthesizing findings into compelling narratives that support strategic recommendations.

• Insight Dissemination: Demonstrate how you communicate research findings and recommendations to various stakeholders, ensuring understanding and driving action through presentations, reports, and workshops.

📝 Enhancement Note: For a senior UX Researcher role, especially at a company like Amazon, a strong portfolio is non-negotiable. It should not just list past work but tell a story about the researcher's process, problem-solving skills, and ability to drive tangible outcomes. The emphasis on "demonstrable impact" and "measurable impact" is crucial, requiring candidates to quantify the value of their research.

💵 Compensation & Benefits

Salary Range:

• Sunnyvale, CA: $167,400 - $226,500 USD annually

• New York, NY: $167,400 - $226,500 USD annually

• Arlington, VA: $152,200 - $205,900 USD annually

• Herndon, VA: $152,200 - $205,900 USD annually

• Seattle, WA: $152,200 - $205,900 USD annually

Benefits:

• Comprehensive Health Coverage: Medical, dental, and vision insurance plans.

• Prescription Drug Coverage: Includes Basic Life & AD&D insurance with the option for Supplemental life plans.

• Mental Health & Wellness Support: Employee Assistance Program (EAP), Mental Health Support, and Medical Advice Line.

• Financial Well-being: Flexible Spending Accounts (FSAs) for healthcare and dependent care.

• Family Support: Adoption and Surrogacy Reimbursement coverage.

• Retirement Savings: 401(k) plan with company matching.

• Work-Life Balance: Generous Paid Time Off (PTO) and Parental Leave policies.

• Equity & Incentives: Sign-on payments and Restricted Stock Units (RSUs) as part of the overall compensation package.

Working Hours:

• Standard full-time work hours are typically 40 hours per week. While the role is on-site, there may be occasional flexibility required for critical research deadlines or global team coordination, which is common in large tech organizations.

📝 Enhancement Note: Amazon's compensation structure is highly competitive, often including a significant equity component (RSUs) on top of base salary and sign-on bonuses. The salary ranges provided are broad, reflecting the typical Amazon practice of adjusting compensation based on specific location, candidate experience, and negotiation. The listed benefits are extensive and align with industry standards for major tech companies, with a strong emphasis on health, family, and financial well-being. The salary ranges were derived from the provided location-specific data.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Cloud Computing, Artificial Intelligence, E-commerce)

Company Size: Extremely Large (Amazon is one of the world's largest employers, with hundreds of thousands of employees globally). This scale offers immense resources, opportunities for impact, and exposure to diverse projects, but also means navigating complex organizational structures.

Founded: 1994. Amazon's long history is marked by a relentless focus on customer obsession, innovation, and operational excellence. This foundational ethos permeates its culture.

Team Structure:

• AWS Applied AI Solutions: This team operates within Amazon Web Services (AWS), focusing on building next-generation intelligent business applications. It's a highly technical and innovative group.

• Cross-Functional Collaboration: The UX Research team is embedded within this larger product development organization. They work intimately with Product Managers, Designers, Software Engineers, and Applied Scientists, functioning as a crucial bridge between customer needs and technical execution.

• Reporting: Senior UX Researchers typically report into a UX Research Manager or Lead, who oversees the research function within a broader product group. The role requires reporting up to senior leadership on research findings and strategic recommendations.

Methodology:

• Data-Driven Decision Making: Amazon heavily relies on data, both quantitative and qualitative, to inform decisions. UX research is critical in providing the qualitative depth to complement quantitative metrics.

• Customer Obsession: The core principle is to start with the customer and work backward. UX Research is paramount in understanding customer needs, pain points, and behaviors to guide product development.

• Bias for Action & Iteration: Teams often operate in an Agile or similar iterative framework. Research needs to be adaptable, providing timely insights to support rapid development cycles while also conducting deeper foundational studies.

• Invent and Simplify: A key Amazon leadership principle, encouraging innovation and finding efficient solutions. This applies to how research is conducted and how insights are delivered.

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

📝 Enhancement Note: Amazon's culture is defined by its Leadership Principles. Understanding these (Customer Obsession, Ownership, Invent and Simplify, Are Right, A Lot, Learn and Be Curious, Hire and Develop the Best, Insist on the Highest Standards, Think Big, Bias for Action, Frugality, Earn Trust, Dive Deep, Have Backbone; Disagree and Commit, Deliver Results) is crucial for success and interview preparation. The scale of Amazon means that while opportunities are vast, navigating the organization and influencing change requires strong communication and strategic thinking.

📈 Career & Growth Analysis

Operations Career Level: Senior Individual Contributor (IC). This level signifies a researcher who can independently lead complex, ambiguous research programs, mentor others, and significantly influence product strategy. They are expected to operate with a high degree of autonomy and deliver high-impact insights.

Reporting Structure: Typically reports to a UX Research Manager or Lead within the AWS Applied AI Solutions group. This manager will likely oversee a portfolio of research activities and researchers. The role involves frequent interaction and collaboration with senior stakeholders across Product, Design, Engineering, and Applied Science.

Operations Impact: The impact of this role is direct and substantial. By understanding customer needs and behaviors related to AI-powered business applications, the Sr. UX Researcher helps ensure that AWS is building solutions that are not only technically sound but also desirable, usable, and valuable to customers. This directly influences product adoption, customer satisfaction, and ultimately, AWS's market leadership in AI solutions. The insights generated can de-risk significant product investments and shape the strategic direction of AI product development.

Growth Opportunities:

• Lead Researcher / Principal UX Researcher: Progression to a Principal level role, taking on even larger and more complex research domains, potentially leading research for entire product lines or strategic initiatives.

• Research Management: Transition into a management role, leading and developing a team of UX researchers, focusing on people management, strategic direction of the research function, and organizational impact.

• Specialization: Deepen expertise in specific areas of AI research, specific methodologies, or strategic research planning within AWS.

• Cross-Organizational Moves: Opportunities to move to other AWS teams or Amazon product areas requiring senior UX research expertise, broadening experience across Amazon's vast product portfolio.

• Thought Leadership: Contribute to internal best practices, present at industry conferences, and become a recognized expert in applied AI research.

📝 Enhancement Note: The "Senior" title at Amazon implies a significant level of responsibility and impact. Candidates are expected to be strategic thinkers, proactive problem-solvers, and influential communicators, not just executors of research tasks. The growth paths indicate a clear trajectory for career advancement within Amazon's research organization.

🌐 Work Environment

Office Type: On-site. The role requires regular presence in one of Amazon's major office locations. This facilitates in-person collaboration, spontaneous discussions, and access to on-site resources.

Office Location(s):

• Sunnyvale, California

• New York, New York

• Arlington, Virginia

• Herndon, Virginia

• Seattle, Washington

These locations are major tech hubs, offering access to talent, industry events, and a vibrant professional community.

Workspace Context:

• Collaborative Hubs: Amazon offices are designed to foster collaboration, with open workspaces, meeting rooms, and common areas. Expect a dynamic environment where interaction with colleagues from various disciplines is common.

• Tools and Technology: Access to cutting-edge research tools, collaboration software, and potentially internal Amazon-developed platforms for data analysis and research management. The team is working with AI solutions, so exposure to advanced computational resources is likely.

• Team Interaction: Frequent opportunities to engage with UX designers, product managers, engineers, and applied scientists, both formally in meetings and informally through day-to-day interactions. The on-site nature encourages a strong sense of team cohesion.

Work Schedule:

• The standard work schedule is typically 40 hours per week, Monday through Friday. While on-site, there might be flexibility in start/end times, but core hours are generally expected for team collaboration. Occasional work outside standard hours may be necessary to meet project deadlines or accommodate global team schedules, especially given Amazon's worldwide operations.

📝 Enhancement Note: The on-site requirement emphasizes Amazon's preference for in-person collaboration, particularly for roles involving complex problem-solving and team integration. Candidates should be prepared for a fast-paced, dynamic office environment common in large tech companies.

📄 Application & Portfolio Review Process

Interview Process:

The interview process at Amazon is rigorous and typically involves several stages:

• Initial Screen: A recruiter or hiring manager will conduct an initial phone screen to assess basic qualifications, interest, and fit.

• Technical/Research Screen: An interview with a UX Researcher or a cross-functional team member focusing on research methodologies, past project experience, and problem-solving skills.

This may involve discussing portfolio items.

• On-Site (or Virtual On-Site) Loop: This typically consists of 4-6 interviews, each lasting about 45-60 minutes, covering various aspects:

  • Research Methodology & Execution: Deep dives into how you approach study design, data collection, analysis, and reporting. Expect to be asked about specific research challenges you've faced and how you overcame them.
  • Behavioral & Situational Questions: Questions based on Amazon's Leadership Principles (e.g., "Tell me about a time you had to Dive Deep," "Describe a situation where you Invented and Simplified"). Prepare STAR (Situation, Task, Action, Result) method responses.
  • Portfolio Review: A dedicated session where you present 1-3 key projects from your portfolio, detailing your role, process, insights, and impact.
  • Cross-Functional Collaboration: Questions about how you work with Product Managers, Engineers, and Designers, and how you handle disagreements or differing priorities.
  • Mentorship/Leadership: If applicable for a senior role, questions about how you mentor junior team members or influence broader research practices.

• Hiring Manager Debrief: A final conversation with the hiring manager to discuss overall fit, career aspirations, and confirm alignment with the role and team.

Portfolio Review Tips:

• Focus on Impact: For each project, clearly articulate the problem, your specific contributions, the methodology, the key insights, and most importantly, the tangible impact or outcome of your research. Quantify results where possible (e.g., "led to a 15% increase in adoption," "reduced task completion time by X%," "informed the prioritization of Y feature").

• Structure Your Narrative: Use a clear story arc for each case study: Problem -> Your Approach (Methodology, Process) -> Findings -> Recommendations -> Impact/Outcome.

• Tailor to the Role: Highlight projects that demonstrate experience with complex, ambiguous problems, quantitative analysis, and ideally, technical or AI-related domains. Showcase your ability to work with cross-functional teams.

• Be Prepared for Deep Dives: Anticipate detailed questions about your methodology choices, data analysis techniques, and how you handled challenges or unexpected findings.

• Visuals are Key: Use clean, professional visuals to illustrate your process and findings. Avoid overwhelming slides with text.

Challenge Preparation:

• Leadership Principles: Thoroughly study and prepare STAR-method responses for common Amazon Leadership Principles. Identify specific examples from your career that showcase each principle.

• Problem Solving: Practice articulating your thought process for tackling ambiguous problems. Be ready to brainstorm research approaches for hypothetical scenarios related to applied AI solutions.

• Data Interpretation: Be prepared to discuss how you interpret and synthesize both qualitative and quantitative data, and how you draw actionable conclusions.

• Collaboration Scenarios: Think about how you would handle disagreements with stakeholders, advocate for user needs, and build consensus within a cross-functional team.

📝 Enhancement Note: Amazon's interview process is known for its depth and focus on its Leadership Principles. Candidates must prepare thoroughly, not just on research skills but also on demonstrating cultural fit and strategic thinking. The portfolio review is a critical component, requiring candidates to showcase not just what they did, but why it mattered and what the results were.

🛠 Tools & Technology Stack

Primary Tools:

• User Research Platforms: Familiarity with tools for remote testing, usability studies, and participant recruitment (e.g., UserTesting.com, Lookback, Maze, UserZoom).

• Survey Tools: Proficiency in creating and deploying surveys for data collection (e.g., Qualtrics, SurveyMonkey, Google Forms).

• Collaboration Suites: Extensive use of tools like Confluence (for documentation), Jira (for tracking), and internal Amazon tools for project management and communication.

• Prototyping Tools: While not a design role, understanding how prototypes are created and used in research (e.g., Figma, Sketch, Adobe XD) is beneficial.

Analytics & Reporting:

• Data Analysis Software: Experience with statistical software (e.g., SPSS, R, Python libraries like Pandas/SciPy) for quantitative analysis.

• Visualization Tools: Ability to create clear and impactful data visualizations (e.g., Tableau, Power BI, or even advanced Excel/Google Sheets features).

• Product Analytics: Familiarity with product analytics platforms (e.g., Amplitude, Mixpanel, or internal AWS analytics tools) to understand user behavior in live products.

CRM & Automation:

• While not directly managing CRM systems, understanding how user data is stored and accessed within CRM or customer data platforms (CDPs) can be advantageous for participant recruitment and understanding customer segments.

• Awareness of how research findings can inform automation strategies for customer engagement or internal processes.

📝 Enhancement Note: While the specific tools are not listed, the description implies a need for proficiency in a broad range of industry-standard UX research and data analysis tools. The emphasis on "Applied AI Solutions" suggests potential exposure to or interest in tools that leverage AI for research insights or analysis. Candidates should be ready to discuss their experience with any of these types of tools.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: Every decision and action is driven by understanding and meeting customer needs. For a UX researcher, this means advocating fiercely for the user's perspective.

• Invent and Simplify: Encouraging innovative solutions and streamlining complex processes. This applies to research methodologies and how insights are delivered.

• Dive Deep: Going beyond surface-level understanding to gain a comprehensive grasp of issues. This is fundamental to rigorous UX research.

• Learn and Be Curious: A continuous drive to learn, explore new ideas, and understand evolving user behaviors and technologies, especially in the rapidly changing field of AI.

• Deliver Results: A focus on achieving tangible outcomes and driving business impact through research.

Collaboration Style:

• Highly Collaborative & Cross-Functional: Expect to work closely with diverse teams. Collaboration is not just encouraged but essential for success, requiring strong interpersonal skills and the ability to build relationships across disciplines.

• Data-Informed & Evidence-Based: Discussions and decisions are grounded in data and research findings. Researchers are expected to present compelling evidence to support their recommendations.

• Direct & Transparent Communication: Amazon values direct feedback and open communication. While respectful, discussions can be candid, with a focus on finding the best solutions.

• Iterative & Agile: The team likely operates in a fast-paced environment, requiring flexibility and adaptability in research approaches to support iterative product development.

📝 Enhancement Note: Alignment with Amazon's Leadership Principles is paramount. Candidates should demonstrate how their personal values and work style align with these principles, particularly Customer Obsession, Invent and Simplify, and Dive Deep, as they directly relate to the core functions of a UX Researcher.

⚡ Challenges & Growth Opportunities

Challenges:

• Ambiguity in AI: Navigating the inherent complexity and rapidly evolving nature of Applied AI solutions, where user understanding and adoption patterns may be less established than with traditional software.

• Balancing Foundational vs. Tactical Research: Managing competing demands for deep, foundational understanding of user needs versus rapid, evaluative research to support immediate product iterations.

• Influencing Technical Stakeholders: Effectively communicating user needs and research insights to highly technical audiences (Applied Scientists, Engineers) who may have different priorities or perspectives.

• Global Scale & Diversity: Conducting research that is relevant and applicable across diverse global markets and user segments, requiring cultural sensitivity and methodological adaptation.

• Measuring Impact: Clearly demonstrating the ROI and impact of UX research in a highly data-driven environment, especially when dealing with complex AI products.

Learning & Development Opportunities:

• AI & ML Domain Expertise: Deepen knowledge in Artificial Intelligence and Machine Learning, understanding user interaction patterns, trust factors, and adoption barriers specific to AI-powered applications.

• Advanced Methodologies: Opportunity to explore and implement cutting-edge research techniques, including those leveraging AI for research itself (e.g., AI-assisted analysis, synthesis).

• Cross-Disciplinary Learning: Gain in-depth understanding of product management, software engineering, and applied science processes within a leading cloud computing organization.

• Leadership & Mentorship: Develop leadership skills through mentoring junior researchers and potentially leading larger research initiatives or programs.

• Industry Exposure: Potential to present findings at internal forums or external conferences, contributing to the broader UX and AI research community.

📝 Enhancement Note: The challenges are inherent to working at the forefront of AI technology within a large, complex organization. Embracing these challenges as learning opportunities is key. The growth potential is significant, offering avenues for deep specialization and leadership.

💡 Interview Preparation

Strategy Questions:

• Research Strategy for Ambiguous AI Problems: "Imagine we're launching a new AI feature for [specific business function]. How would you approach understanding customer needs and potential adoption barriers for this feature, given it's a novel application of AI?" (Focus on defining the problem space, identifying key unknowns, proposing a phased research approach, and key metrics for success).

• Influencing Product Roadmaps: "Describe a time your research findings significantly influenced a product roadmap or a major product decision. What was the situation, how did you present your findings, and what was the outcome?" (Prepare a strong STAR example emphasizing impact and stakeholder management).

• Handling Conflicting Stakeholder Needs: "You've conducted research that contradicts a key assumption held by the engineering or product team. How would you present these findings and advocate for the user's perspective?" (Focus on data-driven communication, empathy, and collaborative problem-solving).

Company & Culture Questions:

• Leadership Principles: "Tell me about a time you had to Dive Deep to understand a complex user behavior." or "Describe a situation where you Invented and Simplified a research process." (Prepare multiple STAR examples for various principles).

• Customer Obsession in Practice: "How do you ensure customer needs are at the forefront of product development, especially when dealing with technical constraints or innovative AI concepts?"

• Team Dynamics: "Describe your ideal working relationship with Product Managers, Designers, and Engineers. How do you build trust and ensure effective collaboration?"

Portfolio Presentation Strategy:

• Quantify Your Impact: For each project, be ready to state the business problem, your specific role, the research methods used, key insights, your recommendations, and the measurable impact of your work. Use numbers wherever possible (e.g., "this insight led to a redesign that improved task completion rates by X%," "our research informed the prioritization of Y features, contributing to Z adoption.").

• Tell a Compelling Story: Structure your presentation like a narrative: set the scene (the problem), introduce the hero (your research approach), build tension (the findings), and resolve with a satisfying conclusion (impact and recommendations).

• Showcase Your Process: Be prepared to walk through your methodology choices, explain why you selected certain methods, and how you analyzed the data.

• Focus on Ambiguity & AI: If possible, highlight projects where you tackled complex, ill-defined problems or research related to AI/technical domains.

📝 Enhancement Note: Preparation should focus heavily on Amazon's Leadership Principles and the ability to demonstrate impact through concrete examples. For a senior role, expect questions that probe strategic thinking, influencing skills, and the ability to operate autonomously in complex domains like AI.

📌 Application Steps

To apply for this Sr. UX Researcher position:

• Submit your application through the official Amazon Jobs portal via the provided URL.

• Portfolio Customization: Curate your portfolio to highlight 2-3 of your most impactful research projects. Prioritize those demonstrating leadership, complex problem-solving, quantitative analysis, and ideally, experience with technical or AI-related domains. Ensure each project clearly articulates the problem, your role, methodology, insights, recommendations, and measurable impact.

• Resume Optimization: Tailor your resume to emphasize keywords from the job description, such as "User Research," "Applied AI," "Ethnography," "Quantitative Analysis," "Product Strategy," "Stakeholder Management," and "Mentoring." Clearly list your years of experience and specific methodologies used.

• Interview Preparation: Thoroughly review Amazon's Leadership Principles and prepare STAR method responses for common behavioral questions. Practice articulating your research process and impact for your portfolio presentations. Be ready to discuss your approach to research strategy in ambiguous AI contexts.

• Company Research: Familiarize yourself with AWS's mission, its Applied AI Solutions offerings, and Amazon's overall Leadership Principles. Understand how UX research contributes to Amazon's customer-centric approach and innovation culture.

⚠️ 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.