Senior UX Researcher, Machine Learning

📍 Job Overview

Job Title: Senior UX Researcher, Machine Learning

Company: Etsy

Location: Brooklyn, New York, United States

Job Type: Full-Time

Category: User Experience Research / Machine Learning Operations

Date Posted: January 06, 2026

Experience Level: 5-10 Years

Remote Status: Hybrid (1-2 days in office)

🚀 Role Summary

• Drive user-centered design and product strategy for Etsy's core machine learning (ML) systems, including Search, Recommendations, and Ads, by deeply understanding user needs and behaviors.

• Bridge the gap between qualitative human insights and quantitative data science to inform the development, evaluation, and optimization of ML models.

• Design and execute mixed-methods research initiatives that uncover how users interact with algorithmic features, ensuring these systems align with user intent, delight, and trust.

• Translate complex data and user feedback into actionable recommendations for cross-functional teams, including ML engineers, data scientists, designers, and product managers.

📝 Enhancement Note: This role is positioned within the ML product development lifecycle, focusing on the user experience of AI-powered features. While not a traditional "Revenue Operations" or "Sales Operations" role, it shares significant overlap in driving business outcomes through data-informed strategy, process optimization (of ML models), and cross-functional collaboration. The emphasis on understanding user behavior to improve algorithmic performance directly impacts buyer engagement and seller success, thus influencing revenue.

📈 Primary Responsibilities

• Design, lead, and execute generative and evaluative user research studies, employing a blend of qualitative methods (e.g., contextual inquiry, usability testing, diary studies, in-depth interviews) and quantitative methods (e.g., survey design and analysis, journey mapping, data-driven segmentation).

• Partner closely with Machine Learning Engineers and Data Scientists to identify critical user insights that can directly inform ML model training, evaluation criteria, and ongoing optimization efforts.

• Investigate and articulate user understanding, interpretation, and trust in algorithmic outputs such as recommendations, search results, and ad placements.

• Synthesize attitudinal data from qualitative research with behavioral and analytics data to pinpoint high-impact opportunities for improving ML model performance and user experience.

• Develop and implement robust evaluation frameworks that integrate quantitative metrics (e.g., relevance, engagement, fairness) with crucial human experience measures (e.g., trust, satisfaction, delight).

• Craft compelling narratives and clear, concise guidance for cross-functional partners by translating complex data, user quotes, and behavioral trends into actionable product and ML development strategies.

• Contribute to the ethical considerations and human-centered aspects of AI development, ensuring transparency, fairness, and trustworthiness in ML-powered features.

📝 Enhancement Note: The responsibilities emphasize a hybrid approach to research, combining deep qualitative understanding with rigorous quantitative analysis. This is critical for ML-focused roles where user behavior is influenced by complex algorithms. The call for developing evaluation frameworks that blend metrics like "relevance" and "fairness" with "trust" and "satisfaction" highlights the unique challenges of researching AI systems.

🎓 Skills & Qualifications

Education:

Experience:

• A minimum of 3+ years of progressive experience leading user research initiatives in complex, data-driven product environments.

• Demonstrated success in influencing product and engineering decisions through impactful research findings.

Required Skills:

• Fluency in designing and conducting a wide range of qualitative user research methodologies, including contextual inquiry, usability testing, in-depth interviews, and diary studies.

• Proficiency in designing and analyzing quantitative research, including survey design, statistical analysis, and journey mapping.

• Strong experience with database querying languages, specifically BigQuery and SQL.

• Familiarity with statistical programming languages such as R or Python for advanced data analysis.

• Experience researching and understanding user interactions with AI-powered products, including a robust grasp of underlying technologies and unique user challenges.

• A solid understanding of concepts like human-in-the-loop workflows, reinforcement learning from human feedback (RLHF), or model interpretability.

• Exceptional ability to communicate insights through engaging storytelling, effectively translating data, quotes, and trends into clear guidance for diverse stakeholders.

Preferred Skills:

• Experience researching or supporting Search, Recommendation, or Ads systems within large-scale marketplace platforms.

• Experience with data-driven segmentation techniques.

• Familiarity with A/B testing frameworks for evaluating ML model performance in production.

📝 Enhancement Note: The emphasis on both qualitative and quantitative skills, coupled with specific tool requirements like SQL and R/Python, is standard for senior research roles in tech. The inclusion of AI-specific concepts like RLHF and model interpretability is a key differentiator for this ML-focused position. The preference for marketplace experience and advanced degrees signals a desire for deep domain and methodological expertise.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a strong track record of research projects that have directly informed product strategy and led to measurable improvements in user experience and/or business metrics.

• Showcase examples of research that successfully integrated qualitative and quantitative data to provide a holistic understanding of user needs and behaviors.

• Highlight instances where research insights were effectively translated into actionable recommendations for engineering and product teams, particularly those working with complex systems or algorithms.

Process Documentation:

• Provide examples of how you've documented research processes, including research plans, participant recruitment strategies, data analysis methodologies, and reporting templates.

• Showcase your approach to creating clear and concise research reports or presentations that effectively communicate complex findings to both technical and non-technical audiences.

• Illustrate how your research has contributed to the iterative development and optimization of product features, particularly those leveraging machine learning or algorithmic components.

📝 Enhancement Note: For senior research roles, especially those involving complex technologies like ML, a portfolio is essential. The emphasis here is on demonstrating the impact of research, the ability to integrate diverse data sources, and the skill in translating findings into actionable strategies for technical teams. This aligns with operations principles of process efficiency and outcome-driven results.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Equity Package: Eligibility for stock options or grants, providing long-term financial upside.

• Annual Performance Bonus: Opportunity for additional compensation based on individual and company performance.

Working Hours:

• Standard full-time hours (approximately 40 hours per week), with flexibility to manage research timelines and project needs. The hybrid work model requires 1-2 days of in-office presence per week for those within commuting distance of Brooklyn, NY, or the San Francisco Bay Area.

📝 Enhancement Note: The salary range is provided, which is helpful for candidates. The benefits listed are standard for senior tech roles and align with competitive offerings in the market. The mention of hybrid work and proximity requirements is crucial for candidates to understand. For operations roles, understanding the balance between structured work hours and the flexibility needed for data analysis and project management is key.

🎯 Team & Company Context

🏢 Company Culture

Industry: E-commerce / Marketplace Technology

Company Size: Over 1,000 employees. This size indicates a mature organization with established processes and a significant user base, offering complex challenges and opportunities for impact. For operations professionals, this means potential for structured career paths, larger datasets, and a need for scalable solutions.

Founded: 2005. Etsy's long history suggests stability, a proven business model, and deep experience in its market. This longevity often correlates with a culture that values thoughtful innovation and long-term strategy.

Team Structure:

• The role sits within the Search, Recommendations, and Ads ML teams, which are part of a broader product and engineering organization. This implies close collaboration with ML engineers, data scientists, product managers, and designers.

• Reporting structure is to a Product Research Manager, indicating a dedicated research function within the product development lifecycle.

Methodology:

• Emphasis on a data-driven approach, blending rigorous statistical analysis with deep qualitative understanding to inform ML model development.

• Focus on iterative improvement and optimization of ML systems based on user feedback and performance metrics.

• A commitment to making technology "human, transparent, and trustworthy," suggesting a culture that prioritizes ethical AI and user well-being.

Company Website: https://www.etsy.com/

📝 Enhancement Note: Etsy's positioning as a marketplace for unique and creative goods provides a distinct context for ML research. The focus on "Keeping Commerce Human" suggests a culture that values authenticity and the human element, even within advanced technology like ML. This is a crucial differentiator for candidates to consider.

📈 Career & Growth Analysis

Operations Career Level: Senior Individual Contributor. This level signifies a high degree of autonomy, expertise, and the ability to lead complex initiatives, mentor junior team members, and influence strategic decisions. For operations, this means contributing to high-level process design and optimization.

Reporting Structure: Reports to a Product Research Manager. This implies a supportive management structure with opportunities for guidance and career development within the research discipline.

Operations Impact: This role has a significant, direct impact on revenue and business outcomes by improving the effectiveness of ML-powered features that drive buyer discovery, engagement, and conversion. Better search, recommendations, and ads directly translate to increased sales for sellers and a more satisfying experience for buyers, fueling the platform's growth.

Growth Opportunities:

• Specialization: Deepen expertise in UX research for AI/ML, becoming a go-to expert within Etsy for human-centered ML development.

• Leadership: Potential to move into a Lead UX Researcher role, managing research strategy for larger product areas or mentoring a team of researchers.

• Cross-functional Mobility: Develop strong relationships and understanding across product management, data science, and engineering, potentially leading to roles with broader product strategy responsibilities.

• Learning: Access to industry conferences, internal training, and opportunities to experiment with new research methodologies and AI technologies.

📝 Enhancement Note: The "Senior" title in a tech company like Etsy implies a significant level of responsibility and impact, akin to a Lead or Principal role in some operations functions. The growth opportunities highlight pathways for specialization in a cutting-edge field and potential leadership within the research or product domain.

🌐 Work Environment

Office Type: Hybrid work model. Employees within commuting distance of Brooklyn, NY, or the San Francisco Bay Area are expected to be in the office 1-2 days per week. This offers a blend of remote flexibility and in-person collaboration.

Office Location(s): Brooklyn, New York (primary mentioned hub), with consideration for candidates in the San Francisco Bay Area.

Workspace Context:

• A collaborative environment where UX researchers work closely with ML engineers, data scientists, designers, and product managers. This fosters a dynamic exchange of ideas and perspectives.

• Access to sophisticated tools and technologies essential for ML research, including data analysis platforms, research software, and potential access to internal ML development environments.

Work Schedule:

• Standard full-time hours are expected, with inherent flexibility common in research roles to accommodate study planning, data collection, and analysis. The hybrid model allows for focused work at home and collaborative sessions in the office.

📝 Enhancement Note: The hybrid model is a critical aspect for candidates evaluating work-life balance and collaboration preferences. For operations roles, understanding the balance between structured work environments and the need for deep analytical focus is important. The mention of specific office hubs is practical for candidates to assess commute feasibility.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will review your application, focusing on qualifications and alignment with the role's core requirements.

• Hiring Manager Interview: A conversation with the Product Research Manager to assess your experience, research philosophy, and fit with the team's objectives.

• Research Portfolio Presentation: You will likely present a selection of your past research work, focusing on projects relevant to ML, data-driven insights, and cross-functional collaboration. This is a critical stage to demonstrate your impact and methodology.

• Cross-Functional Interviews: Meetings with ML Engineers, Data Scientists, Designers, and Product Managers to evaluate your ability to collaborate, communicate complex ideas, and integrate user insights into technical development.

• Skills Assessment/Case Study: Potentially a take-home assignment or a live exercise to assess your research design, analysis, or communication skills in a simulated scenario relevant to ML products.

• Final Interview: Possibly a conversation with a senior leader to discuss strategic fit and long-term potential.

Portfolio Review Tips:

• Select Impactful Projects: Choose 2-3 projects that best showcase your ability to conduct research for complex, data-driven products, ideally those involving ML or algorithmic components.

• Structure Your Narratives: For each project, clearly articulate the problem statement, your research objectives, the methodologies used, your key findings, and, most importantly, the impact of your research on product decisions and business outcomes. Use metrics where possible.

• Highlight Mixed-Methods Approach: Emphasize how you integrated qualitative and quantitative data to provide a comprehensive understanding.

• Showcase Collaboration: Detail how you partnered with engineers, data scientists, and PMs, and how you translated insights for these different audiences.

• Prepare for Questions: Be ready to discuss your decision-making process, challenges encountered, and how you navigated them.

Challenge Preparation:

• Anticipate questions about how you would approach researching a specific ML feature (e.g., understanding user trust in a recommendation engine).

• Be prepared to discuss ethical considerations in AI research and how you would ensure fairness and transparency.

• Practice articulating complex technical concepts in simple, user-centric terms.

• Review Etsy's products, particularly their search and recommendation features, to understand their current user experience.

📝 Enhancement Note: The interview process is typical for senior research roles in major tech companies. The emphasis on portfolio presentation and cross-functional interviews is key. For operations candidates, practicing how to articulate the "impact" of their work using metrics and demonstrating collaboration skills will be crucial.

🛠 Tools & Technology Stack

Primary Tools:

• Database Querying: BigQuery, SQL (essential for accessing and manipulating large datasets).

• Statistical Programming: R, Python (for advanced analysis, modeling, and scripting).

• Qualitative Research Tools: Various platforms for remote usability testing, survey creation (e.g., SurveyMonkey, Qualtrics), and potentially specialized tools for diary studies or interviews.

• Collaboration & Documentation: Tools like Google Workspace (Docs, Sheets, Slides), Confluence, Jira for project management and knowledge sharing.

Analytics & Reporting:

• Data Visualization: Tableau, Looker, or similar tools for creating dashboards and communicating quantitative findings.

• Behavioral Analytics: Tools for tracking user interactions, funnels, and engagement metrics on the Etsy platform.

CRM & Automation:

• While not directly managing CRM/automation in a sales/revenue ops sense, understanding how user data flows from customer interactions into ML models is key. Familiarity with data pipelines and how user feedback loops into system improvements is relevant.

📝 Enhancement Note: The specific tools listed, particularly BigQuery, SQL, R, and Python, are critical. Proficiency in these is non-negotiable for a role that bridges research and data science. For operations professionals, understanding how these tools support data-driven decision-making and process optimization is a transferable skill.

👥 Team Culture & Values

Operations Values:

• Human-Centered Innovation: A core belief in prioritizing the human experience, even when working with advanced technologies like ML. This means advocating for users and ensuring technology serves their needs authentically.

• Data-Informed Decision-Making: A strong reliance on both qualitative and quantitative data to guide product development and strategy, ensuring decisions are grounded in evidence.

• Collaboration & Transparency: Fostering an environment where cross-functional teams work together openly, sharing insights and challenges to achieve common goals.

• Impact & Ownership: A culture that values individuals taking ownership of their work and driving tangible results that benefit buyers and sellers on the platform.

• Diversity, Equity, and Inclusion: Actively promoting an inclusive workplace where diverse backgrounds and perspectives are valued and contribute to innovation.

Collaboration Style:

• Cross-functional Integration: Researchers are expected to be deeply embedded within product teams, working hand-in-hand with engineers, data scientists, designers, and PMs.

• Process Review & Feedback: A culture that encourages constructive feedback on research methodologies, findings, and product strategies to ensure continuous improvement.

• Knowledge Sharing: Encouragement of sharing learnings across teams, potentially through internal presentations, documentation, or informal discussions, to build collective understanding of user behavior and ML capabilities.

📝 Enhancement Note: Etsy's stated values (human-centered, data-informed, collaborative) are highly relevant to operations roles. The emphasis on "Keeping Commerce Human" and ethical AI development provides a unique cultural lens that candidates should appreciate.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of ML Systems: Navigating the intricate nature of ML models and translating their technical workings into understandable user experiences and actionable research insights.

• Balancing Quantitative and Qualitative Data: Effectively integrating diverse data types to form a cohesive understanding, especially when dealing with large-scale behavioral data and nuanced qualitative feedback.

• Measuring Subjective User Experience: Quantifying abstract concepts like trust, delight, and fairness in algorithmic outputs, which are critical for ML model evaluation beyond pure relevance.

• Rapidly Evolving AI Landscape: Staying abreast of advancements in ML and AI technologies and their implications for user research methodologies and product development.

Learning & Development Opportunities:

• Specialized Training: Opportunities to deepen expertise in ML research methodologies, AI ethics, and advanced analytical techniques.

• Industry Engagement: Potential to attend leading conferences (e.g., CHI, UXRConf) and engage with the broader research and AI communities.

• Mentorship: Access to experienced researchers and data scientists within Etsy, fostering skill development and career guidance.

• Experimentation: Encouragement to pilot new research tools, techniques, and approaches to tackle complex ML research questions.

📝 Enhancement Note: The challenges highlight the advanced nature of this role, requiring a blend of technical understanding and research expertise. The growth opportunities are substantial for someone looking to specialize in a high-demand area of technology.

💡 Interview Preparation

Strategy Questions:

• ML Research Design: "How would you design a study to understand user trust in Etsy's personalized recommendation engine? What mixed-methods approach would you employ?" (Prepare by outlining a research plan, including participant criteria, methods, and key questions.)

• Translating Insights: "Describe a time you had to explain complex technical findings (e.g., from ML models) to a non-technical audience. How did you ensure they understood and acted upon your insights?" (Prepare a STAR-method story that highlights your communication and collaboration skills.)

• Problem-Solving: "Imagine users are complaining about irrelevant search results. How would you approach investigating this problem from a user experience perspective, considering the underlying ML algorithms?" (Prepare to discuss your diagnostic process, potential hypotheses, and research methods.)

Company & Culture Questions:

• "What excites you about applying UX research to machine learning at Etsy specifically, given our mission to 'Keep Commerce Human'?" (Research Etsy's mission, values, and recent product developments.)

• "How do you see your role contributing to the collaborative dynamic between UX researchers, data scientists, and ML engineers on this team?" (Prepare to discuss your ideal collaboration style and how you build relationships.)

Portfolio Presentation Strategy:

• Focus on Impact: For each presented project, clearly articulate the business problem, your research objectives, your methodology, key findings, and the concrete actions taken as a result of your research, with measurable outcomes if possible.

• Demonstrate Technical Acumen: Be prepared to discuss the technical aspects of your research projects and how you navigated them, especially if they involved data analysis or complex systems.

• Showcase Collaboration: Highlight instances where you successfully partnered with cross-functional teams and how your research influenced their work.

• Be Ready for Deep Dives: Anticipate questions about your methodological choices, data analysis, and the limitations of your research.

📝 Enhancement Note: Preparation should focus on demonstrating a strong understanding of both research principles and the specific challenges of applying them to ML systems. Highlighting collaboration and impact is crucial.

📌 Application Steps

To apply for this Senior UX Researcher, Machine Learning position at Etsy:

• Submit your resume and an optional cover letter through the Etsy Careers portal via the provided link.

• Portfolio Customization: Tailor your resume and cover letter to highlight your experience in user research for data-driven products, ML/AI, and mixed-methods approaches. Quantify achievements whenever possible.

• Portfolio Preparation: Select 2-3 key projects that best demonstrate your impact, research methodology (especially mixed-methods), and ability to influence product and engineering teams working with complex systems. Practice presenting these concisely, focusing on problem, process, findings, and impact.

• Company & Role Research: Thoroughly research Etsy's mission, values, products (especially their ML-powered features like search and recommendations), and recent news. Understand how your role contributes to their goal of "Keeping Commerce Human."

• Interview Practice: Prepare for behavioral questions using the STAR method, and practice articulating your approach to ML-specific research challenges and collaboration scenarios. Be ready to discuss your portfolio in detail.

⚠️ 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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