Senior Quantitative UX Researcher

📍 Job Overview

Job Title: Senior Quantitative UX Researcher

Company: Trustpilot

Location: London, United Kingdom

Job Type: Full-time

Category: User Experience (UX) Research / Data Science & Analytics

Date Posted: 2026-09-14

Experience Level: 5-10 Years

Remote Status: Hybrid

🚀 Role Summary

• Lead and own the quantitative research agenda for specific product areas, focusing on user experience and trust perception.

• Design and execute large-scale surveys, behavioural analytics, and A/B tests to inform product strategy and business decisions.

• Drive statistical rigour across the UX research team and foster a shared measurement culture with Data Science.

• Translate complex quantitative findings into clear, actionable insights for product managers, designers, and engineers.

• Pioneer and critically evaluate AI-assisted research techniques to enhance the depth and efficiency of quantitative analysis.

📝 Enhancement Note: This role is positioned as a Senior Quantitative UX Researcher, indicating a need for a candidate with a proven track record in designing and executing complex quantitative research initiatives. The emphasis on owning the research roadmap and setting the bar for statistical rigour suggests a leadership component, even if not explicitly a management role. The integration of AI tools signifies a forward-thinking approach to UX research methodology.

📈 Primary Responsibilities

• Design and own the quantitative research roadmap for designated product areas, encompassing attitudinal measurement, behavioural analysis, and longitudinal tracking.

• Lead the execution of large-scale surveys, including the design of validated scales, NPS derivatives, and trust-perception metrics.

• Conduct in-depth analysis of log data and behavioural analytics, focusing on funnels, A/B test outcomes, and cohort/retention modelling.

• Develop and maintain robust measurement frameworks to define and track key performance indicators (KPIs) for critical user journeys, such as review reading and purchase decisions.

• Implement advanced quantitative research methodologies, including experimentation, predictive modelling, and statistical analysis using Python, R, or SQL.

• Pioneer the application of AI-assisted techniques, such as LLM-assisted open-text analysis, while critically assessing their validity, bias, and impact on research outcomes.

• Collaborate closely with Data Science and Analytics teams to establish shared definitions, refine data pipelines, and ensure data integrity.

• Translate statistical outputs and complex data into clear, decision-forcing narratives and compelling presentations for non-technical stakeholders.

• Coach and mentor other UX researchers, enhancing their quantitative methods skills and overall statistical literacy through pairing and constructive feedback.

📝 Enhancement Note: The responsibilities highlight a dual focus on strategic research planning and hands-on execution. The emphasis on "owning the roadmap" and "pioneering AI-assisted techniques" suggests a need for proactive initiative and methodological innovation. The coaching aspect indicates an expectation for mentorship and knowledge sharing within the team.

🎓 Skills & Qualifications

Education: A Bachelor's or Master's degree in a quantitative field such as Psychology, Statistics, Computer Science, Human-Computer Interaction, or a related discipline. A Ph.D. is a plus.

Experience: Minimum of 4+ years of dedicated UX research experience with a strong specialization in quantitative methods and handling complex, broad-scope research projects.

Required Skills:

• Proven expertise in designing and executing advanced quantitative UX research studies.

• Strong command of survey methodology, including instrument design, sampling techniques, and psychometric validation.

• Deep understanding of statistical principles and their application, including regression analysis, factor/cluster analysis, and significance testing.

• Hands-on proficiency in statistical programming languages such as Python, R, or SQL for data analysis and manipulation.

• Direct experience working with large-scale behavioural datasets and analytics platforms (e.g., Amplitude, Looker, BigQuery).

• Demonstrated track record of quantitative research that has demonstrably influenced product development or business decisions.

• Solid understanding of A/B testing methodology, including experimental design and analysis of results.

• Experience experimenting with AI tools within a research workflow (e.g., LLM-assisted analysis, AI-assisted qualitative coding) with a critical eye on validity and bias.

• Excellent communication and interpersonal skills, enabling effective collaboration with diverse cross-functional teams and clear presentation of data-driven findings. Preferred Skills:

• Experience with advanced statistical modelling techniques beyond foundational methods.

• Familiarity with longitudinal study design and cohort analysis.

• Experience in building and maintaining data pipelines for research purposes.

• Ability to critically evaluate and implement new research technologies and methodologies.

• Experience working in a fast-paced, high-growth environment, particularly within a SaaS or platform company.

• Familiarity with qualitative research methods to complement quantitative findings.

📝 Enhancement Note: The qualifications emphasize a blend of deep quantitative expertise, practical tool proficiency, and strong communication skills. The requirement for experience with AI tools suggests a need for candidates who are not only proficient in traditional methods but also curious and adaptable to emerging technologies in research.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase a minimum of 2-3 detailed case studies demonstrating end-to-end quantitative research projects, highlighting your role and impact.

• Clearly articulate the research questions, methodologies employed (surveys, behavioural data analysis, A/B tests), and the rationale behind these choices.

• Present statistical outputs and findings in a clear, digestible format, using visualizations where appropriate.

• Demonstrate how your quantitative insights directly led to specific product improvements, strategic shifts, or measurable business outcomes (e.g., increased conversion rates, improved user satisfaction, reduced churn).

• Include examples of measurement frameworks or dashboards you have developed to track key user journey metrics over time. Process Documentation:

• Detail the process you follow for designing and validating quantitative research instruments, ensuring methodological soundness.

• Explain your approach to data cleaning, analysis, and interpretation, emphasizing statistical rigour and the use of tools like Python, R, or SQL.

• Illustrate your process for collaborating with product, design, and data science teams to define research objectives and integrate findings into the product development lifecycle.

• Provide examples of how you have coached or mentored others in quantitative research methods or statistical analysis.

• Describe your process for critically evaluating and integrating new research tools or techniques, including AI-assisted methodologies, into your workflow.

📝 Enhancement Note: A strong portfolio is crucial for this role, as it serves as tangible proof of the candidate's ability to conduct rigorous quantitative research and translate it into actionable business impact. The emphasis on AI experimentation and statistical rigour means portfolios should highlight these specific skills.

💵 Compensation & Benefits

Salary Range: For a Senior Quantitative UX Researcher in London, with 5-10 years of experience and specialized skills in quantitative methods, Python/R/SQL, and behavioural analytics, the estimated annual salary range is £70,000 - £95,000. This estimate is based on industry benchmarks for similar roles in London, considering the company's FTSE-250 status and the specialized nature of the position.

Benefits:

• A range of flexible working options to support work-life balance.

• Competitive compensation package, including a bonus.

• 25 days of annual holiday, increasing to 28 days after two years of employment.

• Two paid volunteering days per year for community engagement.

• Access to extensive learning and development opportunities through the Trustpilot Academy and Blinkist.

• Comprehensive pension plan and life insurance.

• Health cash plan, including an online GP service and 24/7 support.

• Employee Assistance Programme (EAP) for confidential support.

• Full subscription to Headspace, a mindfulness and meditation app.

• Paid parental leave.

• Season ticket loan and a cycle-to-work scheme for commuting.

• Access to over 4,000 deals and discounts on various products and services.

• Independent financial advice and free standard professional mortgage broker advice.

• Talent acceleration programs for career advancement.

Working Hours: The standard working hours are approximately 40 hours per week, with flexibility offered to accommodate individual needs and promote work-life balance.

📝 Enhancement Note: The salary range is an estimation based on typical market rates for a Senior Quantitative UX Researcher in London. The listed benefits are comprehensive and align with those offered by established tech companies. The mention of flexible working and specific benefits like Headspace and volunteering days indicates a focus on employee well-being.

🎯 Team & Company Context

🏢 Company Culture

Industry: Online Reviews Platform / Software as a Service (SaaS) / Technology. Trustpilot operates within the dynamic B2C and B2B technology sector, focusing on building trust through transparent customer feedback. This context means the operations team must be agile, data-driven, and attuned to user behaviour and business needs.

Company Size: Over 1000 employees. This size indicates a well-established organization with structured processes, but likely still maintains a degree of agility and innovation typical of growth-stage tech companies. For operations professionals, this means opportunities for process refinement and cross-functional impact.

Founded: 2007. With over 15 years in operation, Trustpilot has a mature understanding of its market and customer needs, providing a solid foundation for data-driven decision-making and operational excellence.

Team Structure:

• The UX Research team is likely part of a larger Product or Engineering organization, comprising both qualitative and quantitative researchers.

• This Senior Quantitative UX Researcher will likely report to a Head of UX Research or a Director of Product Insights, with direct collaboration with Product Managers, Designers, Data Scientists, and Engineers.

• The team structure emphasizes cross-functional collaboration, with researchers embedded within product squads or working closely with specific product domains. Methodology:

• Trustpilot emphasizes a data-driven approach, leveraging user research, behavioural analytics, and A/B testing to inform product strategy and development.

• Process optimization and efficiency are key, as indicated by the need for robust measurement frameworks and automated analysis pipelines.

• There's a clear focus on understanding and measuring "trust" as a core product metric, requiring sophisticated quantitative approaches.

Company Website: https://corporate.trustpilot.com/

📝 Enhancement Note: Trustpilot's positioning as a "universal symbol of trust" suggests that operational roles, especially in UX research, must be deeply aligned with understanding and quantifying user perception and behaviour related to trust. The company's size and industry imply a need for scalable, data-intensive operations.

📈 Career & Growth Analysis

Operations Career Level: This is a Senior-level position, indicating a significant degree of autonomy and responsibility. It requires not only execution but also strategic thinking, mentorship, and the ability to influence product direction. It sits above junior or mid-level researchers and may serve as a stepping stone to lead researcher or management roles.

Reporting Structure: The Senior Quantitative UX Researcher will likely report into a Head of UX Research or a similar leadership role within the Product organization. They will collaborate closely with Product Managers, Designers, Data Scientists, and Engineering teams, acting as a key quantitative insight provider for these functions.

Operations Impact: This role has a direct impact on the core Trustpilot product by shaping user experience and understanding trust metrics. The insights generated will influence product strategy, feature development, and ultimately, the company's ability to deliver on its mission of being the universal symbol of trust. Success is measured by the tangible impact of research on product decisions and business outcomes.

Growth Opportunities:

• Specialization: Deepen expertise in advanced quantitative methods, AI-driven research, or specific areas like behavioural economics or predictive modelling.

• Leadership: Transition into a Lead UX Researcher role, taking ownership of larger research domains, or move into a management position, leading a team of researchers.

• Cross-functional Development: Gain broader experience by working on different product areas or collaborating more closely with Data Science on complex analytical projects.

• Industry Influence: Contribute to the broader UX research community through talks, publications, or internal knowledge sharing on best practices in quantitative research and AI application.

📝 Enhancement Note: The "Senior" title implies a strategic contribution beyond just data analysis. The growth opportunities suggest a career path that could lead to specialized expertise, leadership, or broader product strategy involvement, typical for senior operations roles in thriving tech companies.

🌐 Work Environment

Office Type: Trustpilot offers a hybrid work model. Employees are expected to work from the London office for a portion of their week, allowing for in-person collaboration, team building, and access to office amenities, while also providing flexibility for remote work.

Office Location(s): The office is centrally located in London, providing good accessibility via public transport and proximity to amenities. Specific details about the office address and facilities are available on their careers page or upon inquiry.

Workspace Context:

• The office environment is designed to foster collaboration, with amenities like table tennis, a gaming corner, coffee bars, and readily available snacks and refreshments.

• Researchers will have access to modern technology and tools necessary for data analysis and collaboration, including robust computing resources and relevant software.

• Opportunities for informal interaction and knowledge sharing with colleagues across various departments are encouraged through regular social events and team activities.

Work Schedule: While the core working hours are around 40 per week, the hybrid model and flexible working options allow for a degree of autonomy in structuring the work schedule, enabling operations professionals to balance deep analytical work with collaborative sessions and personal commitments.

📝 Enhancement Note: The hybrid work model and office amenities suggest a company culture that values both flexibility and in-person collaboration. For a quantitative researcher, access to necessary tools and a conducive environment for focused work and team interaction are key.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review your application and resume, focusing on your quantitative UX research experience, technical skills (Python/R/SQL), and demonstrated impact.

• Technical Interview: Expect a deep dive into your quantitative methodologies, statistical knowledge, and proficiency with research tools and programming languages. This may involve discussing past projects, problem-solving scenarios, or a live coding/analysis exercise.

• Portfolio Presentation: You will likely be asked to present 1-2 detailed case studies from your portfolio, showcasing your research process, findings, and the impact of your work. Be prepared to discuss your methodology, statistical rigour, and how you translated data into actionable insights.

• Cross-functional Collaboration Interview: This stage will assess your ability to work with Product Managers, Designers, and Data Scientists. You may be asked about how you collaborate, communicate complex findings, and influence decision-making.

• Hiring Manager/Team Interview: A final interview to assess cultural fit, leadership potential (for a senior role), and alignment with Trustpilot's values.

Portfolio Review Tips:

• Quantify Impact: For each case study, clearly articulate the business problem, your research approach, the key quantitative findings, and the measurable impact of your recommendations. Use metrics and data to support your claims.

• Showcase Methodology: Detail your statistical methods, survey design choices, and data analysis techniques. Explain why you chose these methods and how they ensured validity and rigour.

• Demonstrate Tool Proficiency: Highlight your use of Python, R, SQL, or other relevant analytics platforms. If possible, show snippets of code or explain your data processing and analysis pipelines.

• AI Integration: If you have experience with AI-assisted research, clearly explain the tools used, the problem they solved, and how you critically evaluated their output.

• Narrative Flow: Structure your presentations logically, telling a compelling story about how your research drove positive change.

Challenge Preparation:

• Data Interpretation: Be ready to interpret sample datasets or research findings and explain what they mean for the business or product.

• Methodology Justification: Prepare to defend your choice of quantitative methods and statistical approaches.

• Problem-Solving: Practice articulating how you would approach a hypothetical research problem, focusing on quantitative strategies.

• Stakeholder Communication: Rehearse how you would present complex quantitative findings to a non-technical audience.

📝 Enhancement Note: The interview process emphasizes practical application of quantitative skills, portfolio demonstration, and cross-functional collaboration. Candidates should prepare to articulate their methodology, demonstrate tool proficiency, and quantify the impact of their work, especially concerning AI tools.

🛠 Tools & Technology Stack

Primary Tools:

• Statistical Programming: Python, R (essential for advanced analysis, modelling, and automation).

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

• Web Analytics/Behavioural Data Platforms: Amplitude, Looker, Google Analytics, or similar tools for tracking user behaviour, funnels, and event data.

• Data Warehousing: Experience with platforms like Google BigQuery is highly beneficial.

• Survey Platforms: Tools for designing and deploying large-scale surveys (e.g., Qualtrics, SurveyMonkey Enterprise, or custom solutions).

Analytics & Reporting:

• Data Visualization Tools: Tableau, Power BI, or in-tool dashboards within Amplitude/Looker for creating reports and communicating insights.

• A/B Testing Platforms: Tools for designing, running, and analyzing experiments (e.g., Optimizely, VWO, or in-house solutions).

• Statistical Analysis Software: Beyond Python/R, familiarity with SPSS or similar might be useful but less critical.

CRM & Automation:

• While not a direct CRM role, understanding how user data flows from CRM and marketing automation tools into analytics platforms can be beneficial.

• Familiarity with data integration concepts and ETL processes is a plus.

📝 Enhancement Note: Proficiency in Python/R/SQL and experience with behavioural analytics platforms like Amplitude or Looker are critical. The role requires hands-on data manipulation and analysis skills, underpinning the quantitative research function.

👥 Team Culture & Values

Operations Values:

• Data-Driven: Decisions are made based on rigorous analysis and empirical evidence. Operations professionals are expected to champion data integrity and analytical rigour.

• Impact-Oriented: Focus on delivering measurable outcomes that contribute to business goals and enhance user trust. Operations work is valued for its tangible results.

• Collaborative: Strong emphasis on teamwork and cross-functional partnerships with Product, Design, Data Science, and Engineering. Open communication and knowledge sharing are encouraged.

• Curiosity & Innovation: A culture that encourages exploring new methodologies, tools (like AI), and approaches to solve complex problems and drive continuous improvement.

• User-Centricity: At its core, Trustpilot values understanding and improving the user experience, with a specific focus on building and maintaining trust.

Collaboration Style:

• Embedded Teams: Researchers often work within product teams, fostering close working relationships and ensuring research is integrated into the development lifecycle.

• Data-Informed Partnerships: Close collaboration with Data Science and Analytics to ensure alignment on metrics, data definitions, and analytical approaches.

• Clear Communication: A culture that values clear, concise communication of complex findings to diverse audiences, ensuring insights are understood and acted upon.

• Feedback Loops: Openness to providing and receiving constructive feedback, particularly in areas of methodology, analysis, and presentation.

📝 Enhancement Note: Trustpilot's culture appears to be a blend of analytical rigor, collaborative spirit, and a strong user focus, all aimed at building trust. Operations professionals are expected to embody these values, driving impact through data and teamwork.

⚡ Challenges & Growth Opportunities

Challenges:

• Measuring Abstract Concepts: Quantifying "trust" and its impact on user behaviour presents a unique and complex research challenge.

• Data Complexity & Scale: Working with massive behavioural datasets requires sophisticated analytical skills and robust data infrastructure.

• AI Tool Integration: Critically evaluating and effectively integrating nascent AI tools into established research workflows while managing potential biases and validity concerns.

• Influencing Stakeholders: Translating complex statistical findings into compelling narratives that drive product and business decisions across diverse teams.

• Balancing Rigour and Speed: Maintaining high standards of statistical accuracy while working in a fast-paced product development environment.

Learning & Development Opportunities:

• Advanced Analytics Training: Opportunities to deepen expertise in machine learning, predictive modelling, and advanced statistical techniques.

• AI Research Methodologies: Access to training and resources for exploring and applying AI tools in UX research.

• Cross-functional Exposure: Opportunities to learn from Data Scientists, Product Managers, and Designers, broadening understanding of the product lifecycle and business strategy.

• Leadership Development: For those interested, pathways to lead research initiatives, mentor junior researchers, or transition into leadership roles.

• Industry Conferences & Certifications: Support for attending relevant conferences and pursuing professional development certifications in UX research or data science.

📝 Enhancement Note: The challenges presented are inherent to a senior role in a data-intensive, user-focused tech company. The growth opportunities are designed to foster continuous learning and career advancement within the quantitative research and broader operations domain.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you designed and executed a complex quantitative research study that significantly influenced a product decision. What was your methodology, what were the key findings, and what was the ultimate impact?" (Focus on methodology, statistical rigour, and quantified impact.)

• "How would you approach measuring the impact of 'trust' on user behaviour on the Trustpilot platform? What quantitative methods would you employ, and what are the potential challenges?" (Assesses understanding of the core business metric and quantitative problem-solving.)

• "Imagine you're tasked with evaluating a new AI tool for open-text analysis. What criteria would you use to assess its validity, reliability, and potential biases? How would you integrate it into your workflow?" (Tests critical thinking about AI and methodological rigour.) Company & Culture Questions:

• "How do you see quantitative UX research contributing to Trustpilot's mission of becoming the universal symbol of trust?" (Connects your skills to the company's core mission.)

• "Describe your experience collaborating with Data Science and Product Management teams. How do you ensure alignment and effective communication of insights?" (Assesses collaboration and communication skills.)

• "What are your thoughts on the ethical implications of using AI in user research?" (Evaluates awareness of AI's limitations and ethical considerations.) Portfolio Presentation Strategy:

• Storytelling with Data: Structure your case studies as compelling narratives, highlighting the problem, your rigorous quantitative approach, clear findings, and demonstrable business impact.

• Visual Clarity: Use well-designed charts and graphs to present complex data, ensuring they are easy to understand for a mixed audience. Explain what each visualization represents and why it's important.

• Methodological Defence: Be prepared to explain and justify your choice of statistical methods, survey design, and data analysis techniques.

• Quantify Everything: Wherever possible, use numbers and metrics to demonstrate the scale of the problem, the insights gained, and the positive outcomes of your recommendations.

• AI Tool Showcase (if applicable): If you present a project involving AI tools, clearly articulate the problem solved, the tool used, your critical evaluation process, and the benefits realized.

📝 Enhancement Note: Candidates should prepare to articulate their quantitative research process with depth, demonstrate their ability to derive actionable insights, and showcase their critical thinking regarding emerging technologies like AI, all within the context of Trustpilot's mission.

📌 Application Steps

To apply for this Senior Quantitative UX Researcher position:

• Submit your application through the Trustpilot careers portal, ensuring your resume and any accompanying documents are tailored to highlight your quantitative UX research expertise.

• Portfolio Customization: Select 2-3 of your most impactful quantitative research projects to showcase. For each, prepare a concise presentation or document detailing the problem, your rigorous methodology (including statistical techniques and tools used), key findings, and quantified business impact. Explicitly mention any experience with AI-assisted research tools.

• Resume Optimization: Ensure your resume clearly articulates your years of experience, specialization in quantitative methods, proficiency with Python/R/SQL, and experience with behavioural analytics platforms. Use keywords from the job description and quantify achievements wherever possible.

• Interview Preparation: Practice articulating your research process, statistical reasoning, and how you translate data into actionable insights. Prepare specific examples for behavioural questions, focusing on collaboration, problem-solving, and influencing stakeholders. Rehearse your portfolio presentation.

• Company Research: Familiarize yourself with Trustpilot's mission, products, and recent company news. Understand how quantitative UX research, particularly concerning "trust," contributes to their strategic goals. Research their approach to AI and innovation.

⚠️ 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.