User Researcher

📍 Job Overview

Job Title: User Researcher

Company: Notion

Location: San Francisco, California, United States

Job Type: FULL_TIME

Category: User Experience (UX) Research / AI Product Research

Date Posted: 2026-06-18

Experience Level: Mid-Level (3+ years)

Remote Status: Hybrid (3 days in office)

🚀 Role Summary

• Drive product strategy and decision-making through rigorous, end-to-end user research for Notion's AI-powered experiences, including Notion Agent and Chat.

• Investigate and understand complex human-AI workflows, focusing on user goal setting, task delegation, output review, and system orientation.

• Foster deep cross-functional partnerships with Design, Product Management, Engineering, and Data Science to embed user insights throughout the product development lifecycle.

• Translate research findings into compelling narratives and actionable recommendations for diverse stakeholders, utilizing creative communication methods.

• Measure and articulate the business impact of research by connecting insights to measurable outcomes and driving tangible product improvements.

📝 Enhancement Note: This role is specifically focused on the intersection of User Research and Artificial Intelligence, a highly in-demand area within the tech industry. The emphasis on "AI fluency," "systems thinking," and understanding "human-AI workflows" indicates a need for candidates who can not only conduct traditional UX research but also grasp the nuances of AI product development and its impact on user behavior. The hybrid work model with specific "Anchor Days" is a key detail for candidates to consider regarding their work style and location preferences.

📈 Primary Responsibilities

• Design and execute comprehensive quantitative and qualitative research studies across the entire AI product lifecycle, from foundational discovery and concept testing to usability and post-launch evaluation.

• Develop a deep understanding of how users discover, build, and interact with AI features within Notion, identifying pain points and opportunities for enhancement.

• Analyze user behavior and feedback to inform the design and iteration of AI models, user interfaces, and governance mechanisms.

• Collaborate closely with product teams to define research questions, scope studies, and ensure research findings directly influence product roadmaps and feature development.

• Champion user-centricity by effectively communicating research insights and advocating for user needs to ensure AI features are intuitive, effective, and valuable.

• Triangulate insights from qualitative research, behavioral data, and business metrics to build a holistic understanding of user needs and product performance.

• Develop and refine research methodologies tailored to the unique challenges of researching AI products, including studying AI model behavior and user adaptation.

📝 Enhancement Note: The responsibilities highlight a need for researchers who can operate autonomously and deliver impact in a fast-paced, "0→1" environment. The emphasis on "end-to-end research" and "business-minded impact" suggests that this role requires not just research execution but also strategic thinking and a strong understanding of how research contributes to business objectives. The requirement to "tailor storytelling" indicates a need for strong communication and presentation skills beyond just reporting data.

🎓 Skills & Qualifications

Education:

• Master’s or PhD degree in Human-Computer Interaction (HCI), Psychology, Behavioral Economics, Anthropology, Sociology, or a closely related field is preferred.

• A strong foundation in research methodologies is essential, regardless of formal degree. Experience:

• Minimum of 3+ years of professional experience conducting UX research in an industry setting.

• Demonstrated experience in designing and executing both quantitative and qualitative research studies.

• Proven ability to translate research findings into actionable product recommendations and drive measurable impact. Required Skills:

• AI Fluency: Strong understanding of AI concepts, model behavior, uncertainty, system constraints, and how these factors influence product experiences. Hands-on experience with AI products is crucial.

• Systems Thinking: Ability to comprehend complex user journeys and identify breakdowns across interconnected systems, including inputs, model interactions, UI, and governance.

• Pragmatism & Initiative: Aptitude for prioritizing research in fast-moving, ambiguous environments; comfortable with "scrappy" approaches and rapid iteration, as well as in-depth studies.

• Research Craft: Expertise in selecting and applying appropriate qualitative and quantitative research methodologies to address specific research questions.

• Data Fluency: Proficient in using behavioral and business data to inform research, interpret results, and connect insights to measurable outcomes through triangulation.

• Cross-functional Collaboration: Proven ability to build strong working relationships and collaborate effectively with Design, Product, Engineering, and Data Science teams.

• Communication & Storytelling: Excellent verbal and written communication skills, with the ability to tailor insights and narratives to diverse audiences (e.g., via videos, podcasts, workshops).

Preferred Skills:

• Experience researching AI-enabled products, particularly agentic or workflow automation tools.

• Previous experience researching B2B SaaS products.

• Expertise in utilizing specialized AI research tools such as Outset, Listen Labs, or Dovetail.

• Demonstrated experience communicating research insights to executive-level stakeholders.

• Familiarity with the work and philosophies of pioneers like Douglas Engelbart and Alan Kay.

📝 Enhancement Note: The "AI fluency" requirement is a critical differentiator for this role. Candidates should be prepared to discuss their personal and professional engagement with AI tools and how they approach researching AI products. The emphasis on "systems thinking" and "data fluency" suggests a need for a researcher who can see the bigger picture and integrate diverse data sources, not just isolated user feedback. The "3+ years" experience combined with the preference for a Master's/PhD indicates a role that requires a solid theoretical foundation coupled with practical application.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies Demonstrating End-to-End Research: Showcase at least 2-3 detailed case studies that illustrate your ability to manage research projects from initial scoping and methodology design through execution, analysis, and impact reporting.

• AI Product Research Examples: Include specific examples of research conducted on AI-powered features, products, or systems, highlighting your approach to understanding AI-specific user challenges and opportunities.

• Quantitative & Qualitative Synthesis: Demonstrate your ability to integrate findings from both quant (surveys, analytics, usability metrics) and qual (interviews, ethnographic studies, usability tests) methods to provide comprehensive insights.

• Impact & Actionability: Clearly articulate the outcomes of your research, including specific product changes, strategic shifts, or business metrics influenced by your work. Quantify impact where possible (e.g., "led to a 15% increase in feature adoption").

• Cross-functional Collaboration Evidence: Highlight instances where your research directly influenced product, design, or engineering decisions through effective collaboration and communication.

Process Documentation:

• Research Design & Planning: Provide examples of how you structure research plans, define clear objectives, select appropriate methodologies, and plan for participant recruitment and data collection.

• Data Analysis & Synthesis: Showcase your approach to analyzing qualitative (e.g., thematic analysis) and quantitative (e.g., statistical analysis, usability metrics) data, and how you synthesize findings into coherent insights.

• Insight Communication & Dissemination: Demonstrate how you present research findings to various stakeholders, including product managers, designers, engineers, and leadership, using compelling formats (e.g., slide decks, reports, workshops, video summaries).

• Iterative Improvement: Illustrate how your research has informed iterative product development cycles, showing a continuous feedback loop from user insights to product improvements.

📝 Enhancement Note: For this AI-focused User Researcher role, the portfolio should strongly emphasize how candidates approach researching novel AI interactions. Applicants should be ready to discuss their thought process for understanding AI's unique challenges, such as user trust, model explainability, and managing user expectations. Demonstrating the ability to conduct rapid, impactful research ("doing an end-to-end study in a week") will be highly valued.

💵 Compensation & Benefits

Salary Range:

• For roles based in San Francisco or New York City, the estimated base salary range is $164,000 - $190,000 per year.

• This range is determined by factors including location, role scope, complexity, and candidate experience. Benefits:

• Highly Competitive Cash Compensation: Base salary within the specified range.

• Equity: Stock options or grants, providing ownership and participation in the company's growth.

• Comprehensive Health Insurance: Medical, dental, and vision coverage.

• Retirement Savings Plan: Likely a 401(k) or similar plan, potentially with employer matching.

• Paid Time Off (PTO): Generous vacation, sick leave, and holidays.

• Parental Leave: Support for new parents.

• Professional Development: Opportunities for learning, training, and conference attendance.

• Wellness Programs: Initiatives focused on employee well-being.

Working Hours:

• Standard full-time work week, estimated at 40 hours.

• The role requires 3 days per week in the office (Mondays, Tuesdays, Thursdays) for collaborative work, with flexibility for remote work on other days.

📝 Enhancement Note: The salary range provided is specific to the San Francisco/New York City locations. Candidates in other US locations may see a different range. The listed benefits are standard for tech companies of Notion's caliber, with "Equity" being a significant component for long-term incentives. The explicit mention of "Anchor Days" for in-office work is crucial for candidates to understand the hybrid nature of the role.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software / Technology (Collaboration Tools, Productivity Software, AI Workspace)

Company Size: Scale-up / High-Growth (indicated by Ashby 'Company Size' data not provided, but Notion's public profile suggests hundreds to thousands of employees). This means a dynamic environment with evolving processes and a strong focus on innovation.

Founded: 2013. Notion has a proven track record and has evolved significantly since its founding, now heavily integrating AI.

Team Structure:

• AI Product Research Team: Likely a specialized team focused on understanding user interaction with AI features. The role will report into a research lead or manager within this function.

• Cross-Functional Collaboration: This role is designed to work directly and frequently with Product Managers, Designers (UX/UI), Engineers (Software, AI/ML), and Data Scientists. Collaboration is highly integrated.

• Decentralized Decision-Making: While structured, Notion fosters a culture where individuals and teams are empowered to make decisions, especially in fast-moving areas like AI development.

Methodology:

• Data-Driven Decisions: Strong emphasis on using both quantitative and qualitative data to inform product strategy and design.

• Iterative Development: Agile and lean methodologies are likely employed, prioritizing rapid prototyping, testing, and iteration based on user feedback.

• Craftsmanship & Long-Term Vision: A focus on building high-quality, lasting products that set industry standards, particularly in the evolving AI workspace landscape.

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

📝 Enhancement Note: Notion's culture is described as valuing "craft," "building things that last," and the "belief that great work is still fundamentally human." For a User Researcher, this means the company likely values deep understanding of user needs and a commitment to creating genuinely valuable and human-centric AI experiences, rather than just adopting AI for novelty. The mention of "customer zero" suggests employees are encouraged to be early adopters and testers of new features.

📈 Career & Growth Analysis

Operations Career Level: Mid-Level Individual Contributor. This role is for an experienced researcher ready to take ownership of significant research initiatives within the AI product space. It's not an entry-level position, nor is it typically a management track role without further progression.

Reporting Structure:

• The User Researcher will report to a Research Manager or Lead within the Product organization.

• They will work closely with Product Managers, Designers, and Engineering leads for specific AI product initiatives. Operations Impact:

• This role has a direct impact on the success of Notion's AI features, influencing how millions of users interact with AI.

• Insights generated will shape the core AI product strategy, user experience, and ultimately, user adoption and retention for these critical new offerings.

• The researcher will be instrumental in defining what makes Notion's AI unique and valuable in a competitive market. Growth Opportunities:

• Specialization within AI Research: Deepen expertise in researching specific AI product types (e.g., generative AI, agentic workflows, AI assistants) and advanced research methodologies for AI.

• Mentorship & Leadership: Potential to mentor junior researchers or lead research efforts for larger, more complex AI product areas.

• Cross-Functional Influence: Grow influence across Product, Design, and Engineering leadership through impactful research and strategic contributions.

• Advancement to Senior/Staff Researcher: Progression to senior or staff levels involves taking on more complex, ambiguous, and strategic research challenges, potentially leading research direction for entire product pillars.

• Industry Thought Leadership: Opportunities to present at conferences, contribute to internal knowledge sharing, and help shape Notion's approach to AI product research.

📝 Enhancement Note: The growth path for a User Researcher at a company like Notion typically involves deepening expertise and increasing scope of influence. Moving from a mid-level role to a Senior or Staff Researcher would involve tackling more ambiguous problems, driving research strategy for broader product areas, and potentially mentoring others. The specific focus on AI provides a unique avenue for specialization and career development within a cutting-edge field.

🌐 Work Environment

Office Type: Hybrid. Notion operates with specific "Anchor Days" (Mondays, Tuesdays, Thursdays) requiring in-office presence, alongside remote work flexibility. This model aims to balance in-person collaboration with individual focus time.

Office Location(s): San Francisco, California (and New York City, as mentioned in the role description). These are major tech hubs with established office presences.

Workspace Context:

• Collaborative Spaces: Offices are designed to facilitate team interaction, brainstorming, and spontaneous discussions, particularly on Anchor Days.

• Technology & Tools: Access to industry-standard research tools, collaboration software (including Notion itself), and potentially specialized AI research platforms.

• Team Interaction: Frequent opportunities for direct interaction and collaboration with cross-functional peers, fostering a dynamic and integrated work environment.

• Focus Time: While collaboration is key, the hybrid model allows for dedicated remote days for concentrated work, analysis, and writing.

Work Schedule:

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

• The hybrid model requires attendance on specific office days, with flexibility for remote work. Candidates should be comfortable with this structure.

📝 Enhancement Note: The hybrid model with designated "Anchor Days" is a critical aspect of the work environment. Candidates should be prepared for a structured hybrid approach that prioritizes in-person collaboration for specific activities, while still offering remote flexibility. This setup is common in high-growth tech companies seeking to maximize team synergy and innovation.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review applications and conduct a brief introductory call to assess basic qualifications and cultural fit.

• Portfolio Review & Deep Dive: This is a critical stage. You will likely present 1-2 detailed case studies from your portfolio, focusing on your research process, methodologies, findings, and impact. Expect in-depth questions about your approach to AI research, problem-solving, and collaboration.

• Cross-Functional Interviews: Interviews with Product Managers, Designers, and Engineers to assess your ability to collaborate, communicate effectively, and integrate research into their workflows. These may include scenario-based questions or discussions of past collaborations.

• Hiring Manager Interview: A final interview with the hiring manager to discuss your overall experience, career goals, and how you align with the team's strategic objectives and Notion's culture.

• Take-Home Assignment (Potential): Some companies may include a small take-home assignment to assess specific research skills or problem-solving abilities in a simulated scenario.

Portfolio Review Tips:

• Curate Selectively: Choose 2-3 of your strongest, most relevant projects. Prioritize those that showcase AI research, end-to-end process, and measurable impact.

• Structure Your Narrative: For each case study, clearly outline: Problem/Opportunity, Your Role, Research Questions, Methodology (and why), Key Findings, Recommendations, and Impact/Outcome.

• Highlight AI Nuances: Specifically address how you approached researching AI features. Discuss challenges related to AI (e.g., understanding model bias, user trust, explainability) and how you navigated them.

• Emphasize Actionability: Clearly articulate how your research led to tangible product changes or strategic decisions. Quantify impact whenever possible.

• Be Prepared for Deep Dives: Anticipate detailed questions about your methodology choices, data analysis, and how you handled challenging research situations.

• Showcase Communication Skills: Practice presenting your work concisely and engagingly. Be ready to adapt your presentation style for different audiences.

Challenge Preparation:

• AI Product Scenario: Be prepared to discuss how you would research a hypothetical AI feature for Notion, including defining research questions, choosing methods, and anticipating challenges.

• Problem-Solving: Expect questions that require you to break down a complex user problem related to AI and outline a research plan to address it.

• Methodology Justification: Be ready to defend your methodological choices, explaining why a particular approach is best suited for a given research question, especially in the context of AI.

• Stakeholder Management: Practice articulating how you would collaborate with and influence product, design, and engineering teams, especially when research findings might be challenging.

📝 Enhancement Note: The interview process will heavily scrutinize your ability to conduct research in the AI domain. Candidates should prepare to discuss their understanding of AI's unique research challenges and how they apply that knowledge. The portfolio presentation is crucial, so practicing a clear, impact-driven narrative is essential.

🛠 Tools & Technology Stack

Primary Tools:

• Notion: As the core product, deep familiarity and ability to leverage Notion for project management, documentation, and knowledge sharing is implied.

• Research Platforms: Proficiency with tools for qualitative data analysis (e.g., Dovetail, NVivo), quantitative data analysis (e.g., SPSS, R, Python libraries like Pandas/NumPy), survey tools (e.g., SurveyMonkey, Typeform, Google Forms), and usability testing platforms (e.g., UserTesting.com, Lookback).

• Specialized AI Research Tools: Experience with tools like Outset, Listen Labs, or similar platforms for analyzing AI interactions or user feedback on AI features is a strong plus.

Analytics & Reporting:

• Data Analysis Tools: Competency in analyzing user behavior data from product analytics platforms (e.g., Amplitude, Mixpanel, Google Analytics) to inform research questions and interpret findings.

• Data Visualization: Skills in creating clear and compelling visualizations of research data using tools like Tableau, Looker, or built-in features of analytics platforms.

• Business Intelligence Tools: Familiarity with BI dashboards to understand key business metrics and connect research impact to business outcomes.

CRM & Automation:

• While not a direct CRM or automation role, understanding how user feedback and research insights might feed into customer success or product marketing efforts could be beneficial. Familiarity with how CRM data (e.g., Salesforce) can inform user segmentation or provide context for research is a plus.

• Collaboration Tools: Proficiency with tools like Slack, Jira, Confluence, and Figma for cross-functional communication and design collaboration.

📝 Enhancement Note: The emphasis on "data fluency" and "triangulating across data sources" means candidates should be comfortable using a variety of tools. While specific AI research tools are "nice to have," demonstrating a strong foundation in general UX research tools and analytical capabilities is essential. Familiarity with product analytics platforms is key for connecting qualitative findings to quantitative user behavior.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: A deep commitment to understanding and serving user needs, ensuring AI features provide genuine value and enhance productivity.

• Craftsmanship & Quality: A dedication to producing high-quality research and insights that lead to well-designed, robust products.

• Collaboration & Transparency: Open communication, knowledge sharing, and a willingness to work closely with diverse teams to achieve common goals.

• Data-Driven Decision Making: A reliance on evidence and data to guide product strategy and design choices.

• Intellectual Curiosity & Tinkering: An intrinsic drive to explore, learn, and experiment, especially with new technologies like AI, and to foster this in others.

• Pragmatism & Impact: A focus on delivering actionable insights that drive real business outcomes, even in fast-paced, ambiguous environments.

Collaboration Style:

• Integrated Teams: Researchers are embedded within product teams, working hand-in-hand with designers, PMs, and engineers.

• Proactive & Iterative: Collaboration is ongoing, with researchers actively participating in product planning, design reviews, and development sprints.

• Open Feedback Culture: A willingness to share feedback constructively and receive it openly across all levels and functions, fostering continuous improvement.

• Knowledge Sharing: Emphasis on documenting and sharing research findings broadly within the organization to build collective understanding and drive informed decisions.

📝 Enhancement Note: Notion's culture values individuals who are not only skilled researchers but also proactive collaborators and strategic thinkers. The emphasis on "customer zero" and "tinkering" suggests an environment where employees are encouraged to be hands-on with the product and its new AI capabilities, contributing to its evolution.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating AI Ambiguity: Researching rapidly evolving AI capabilities and user adoption patterns presents unique challenges in predicting behavior and defining stable research questions.

• Measuring AI Impact: Quantifying the direct impact of AI features on user productivity, workflow efficiency, and business outcomes can be complex, requiring innovative measurement strategies.

• Balancing Speed and Rigor: In a fast-paced "0→1" environment, balancing the need for rapid insights with the rigor of thorough research methodologies is a constant challenge.

• Communicating Complex AI Concepts: Effectively translating intricate AI concepts and research findings about AI behavior to non-technical stakeholders requires strong communication and storytelling skills.

• Ethical AI Considerations: Researching AI necessitates an awareness of ethical implications, bias, and user trust, requiring researchers to navigate these sensitive areas responsibly.

Learning & Development Opportunities:

• Deep AI Expertise: Gain unparalleled experience researching cutting-edge AI products and workflows, becoming a subject matter expert in human-AI interaction.

• Methodological Innovation: Opportunity to develop and refine novel research methods specifically for AI products.

• Cross-Functional Skill Development: Enhance skills in product strategy, design thinking, and data analysis through close collaboration with diverse teams.

• Industry Conferences & Training: Access to resources for professional development, including conferences, workshops, and specialized AI courses.

• Mentorship: Potential to receive mentorship from senior researchers or leaders within Notion's product organization, guiding career growth.

📝 Enhancement Note: This role offers significant opportunities for growth in a highly relevant and in-demand field. The challenges are inherent to working at the forefront of AI product development, making it an exciting prospect for ambitious researchers.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to conduct research in a highly ambiguous or rapidly changing environment. How did you approach it, and what was the outcome?" (Focus on pragmatism, initiative, and methodology adaptation).

• "How would you approach researching the discoverability and usability of a new AI feature that automates complex user workflows within Notion?" (Focus on research design, methodology selection, and understanding user mental models for AI).

• "Imagine your research reveals that users are hesitant to trust or adopt a new AI feature. How would you investigate the root causes of this distrust and what recommendations might you propose?" (Focus on understanding user psychology with AI, identifying barriers, and proposing actionable solutions). Company & Culture Questions:

• "What interests you most about Notion's mission and its approach to AI?" (Research Notion's product, values, and recent AI developments).

• "How do you see the role of a User Researcher evolving in the age of AI, and how does that align with Notion's vision?" (Demonstrate understanding of AI's impact on UX and your research philosophy).

• "Describe a situation where your research findings were challenged or met with resistance. How did you handle it and ensure your insights were considered?" (Focus on communication, stakeholder management, and data-driven persuasion). Portfolio Presentation Strategy:

• AI Case Study Focus: Select a project that best demonstrates your experience with AI or complex systems. Clearly articulate the problem, your specific contributions, the methods used (and why), key findings, and the tangible impact.

• Quant + Qual Synthesis: Show how you integrated diverse data sources to arrive at your conclusions. Be ready to discuss your analytical process for both qualitative and quantitative data.

• Actionability & Impact Narrative: Emphasize how your research translated into concrete product improvements or strategic decisions. Use metrics to illustrate impact where possible.

• Concise and Engaging Delivery: Practice your presentation to ensure it flows logically, stays within time limits, and highlights your key contributions and thought process effectively. Be prepared for detailed follow-up questions.

📝 Enhancement Note: Candidates should anticipate questions that probe their understanding of AI's unique research considerations, their ability to work pragmatically in a fast-paced environment, and their skill in translating complex insights into actionable product decisions. Demonstrating a proactive and collaborative approach will be crucial for success.

📌 Application Steps

To apply for this User Researcher position:

• Submit your application through the provided link on Ashby.

• Tailor Your Resume: Highlight your 3+ years of UX research experience, emphasizing any work with AI products, complex systems, or B2B SaaS. Use keywords from the job description like "quantitative research," "qualitative research," "AI fluency," "systems thinking," and "product lifecycle research."

• Prepare Your Portfolio: Select 2-3 strong case studies that showcase end-to-end research, your AI research approach, and measurable impact. Ensure clear structure (Problem, Role, Methods, Findings, Recommendations, Impact). Practice presenting these concisely.

• Research Notion: Understand Notion's product, mission, recent AI initiatives (e.g., Notion Agent, Chat), and company values. Be ready to articulate why you are a good fit for their culture.

• Practice Interview Questions: Prepare answers to common UX research behavioral questions, strategy questions related to AI, and questions about your portfolio.

⚠️ 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.