UX Researcher, Mixed Methods

📍 Job Overview

Job Title: UX Researcher, Mixed Methods

Company: Ford Motor Company

Location: Dearborn, MI, United States

Job Type: Full time

Category: User Experience Research & Data Analysis

Date Posted: 2026-09-09T18:39:09

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

Remote Status: Hybrid (4+ days onsite required)

🚀 Role Summary

• Drive product and feature improvements through rigorous mixed-methods UX research and data analysis.

• Support the Voice of the Customer (VoC) program, ensuring customer feedback is systematically collected, analyzed, and actioned.

• Enhance data analysis processes using AI tools and automation to improve efficiency and reporting accuracy.

• Translate complex research findings and customer data into clear, actionable insights for design and product teams.

• Contribute to a data-driven design culture within the Digital Product Design (DPD) team at Ford.

📝 Enhancement Note: This role is positioned within Ford's Digital Product Design (DPD) team, specifically supporting the Customer Confidence Center of Excellence. The hybrid work model with a strong emphasis on onsite presence (4+ days/week) indicates a collaborative team environment requiring in-person interaction for design reviews, brainstorming, and team cohesion. The salary ranges provided suggest a mid-level to senior mid-level position, requiring a blend of hands-on research execution and analytical rigor.

📈 Primary Responsibilities

• Execute a range of qualitative and quantitative research methodologies, including usability studies, heuristic evaluations, concept testing, and usability benchmarking, to assess and refine user experiences.

• Plan and conduct research activities in close collaboration with product designers, product managers, and engineering teams to align with project goals and timelines.

• Analyze qualitative data from research sessions and quantitative data from customer surveys to identify key themes, pain points, and opportunities for improvement.

• Translate research findings into compelling and actionable insights, reports, and presentations for diverse stakeholders, including senior leadership.

• Support the ongoing management and execution of the Customer Experience Survey program, including supplier coordination, survey roadmap planning, sample procurement, and budget oversight.

• Monitor and report on key performance indicators (KPIs) related to customer satisfaction and vehicle experience, conducting competitive benchmarking and in-depth analysis.

• Investigate, manage, and improve data pipelines for customer feedback, supporting data integration initiatives and VOC dashboard updates.

• Document, standardize, and formalize data analysis processes to enhance efficiency, leverage integrated data sources, and reduce time spent on urgent requests.

• Utilize AI tools and LLMs for secondary data analysis, unstructured feedback analysis, data presentation, and process automation to streamline workflows.

📝 Enhancement Note: The responsibilities highlight a dual focus on direct UX research execution and analytical support for a large-scale Voice of the Customer (VoC) program. The emphasis on "improving data analysis process efficiency" and "leveraging integrated data sources" suggests a need for someone who can not only analyze data but also optimize the systems and processes around data collection and reporting. The mention of LLMs and AI tools points towards an expectation for innovative approaches to data analysis and automation.

🎓 Skills & Qualifications

Education:

• Bachelor's degree or equivalent in a research-oriented or quantitative field such as Data Science, Statistics, Computer Science, Psychology, Human Factors, Human-Computer Interaction (HCI), Industrial Design, or a closely related discipline. Experience:

• 1-3 years of direct experience as a User Researcher, Human Factors Specialist, Voice of the Customer (VoC) Analyst, or Customer Experience (CX) Researcher, including experience with on-site research or UX design coordination/project management.

• 1-3 years of experience as a Data Analyst, with a demonstrated track record in data integration and improving process efficiency. Required Skills:

• Advanced proficiency in Microsoft Excel, PowerPoint, and Word for data analysis, reporting, and presentation.

• Experience using AI tools for secondary data analysis, unstructured customer feedback analysis, data presentation, and process automation.

• Working knowledge of both quantitative and qualitative research methods, including survey design, sampling techniques, fielding, data capture, and reporting.

• Familiarity with statistical analysis principles, including hypothesis testing, A/B comparisons, and time series analysis.

• Strong customer empathy and problem-solving skills, with a passion for UX and customer-centered design.

• Excellent communication, presentation, and interpersonal skills, with the ability to present effectively to senior management and cross-functional teams.

• Strong organizational skills with meticulous attention to detail.

• Comfort working with changing demands, early-stage vehicle programs, and emerging technologies.

• Ability to manage multiple tasks with minimal direction and operate as a self-starter. Preferred Skills:

• Experience with Agile development processes and tools like Jira.

• Familiarity with design and research platforms such as Figma, Protopie, Dscout, Qualtrics, or UserTesting.com.

• Experience with SQL for data querying and manipulation.

• Proficiency in advanced statistical modeling techniques (e.g., regression, clustering, machine learning).

• Experience with data visualization tools like Power BI, Tableau, Looker Studio, or advanced Excel VBA.

• Experience with data wrangling (transformation, cleaning, validation) to prepare analysis-ready datasets.

• Experience with R programming for statistical analysis.

• Experience within the automotive industry.

📝 Enhancement Note: The "1-3 years" requirement for both UX Research and Data Analysis suggests a role that is not entry-level but expects foundational experience in both domains. The blend of required and preferred skills indicates a strong preference for candidates who have exposure to modern UX research tools, data visualization platforms, and statistical programming languages, which are common in sophisticated operations and analytics roles.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a portfolio showcasing a blend of qualitative UX research projects and quantitative data analysis initiatives.

• Include case studies detailing the research process, methodologies employed (e.g., usability testing, surveys), and the resulting insights.

• Showcase examples of how your data analysis led to tangible process improvements, efficiency gains, or data-driven product decisions.

• Present work that highlights your ability to manage data pipelines, integrate disparate data sources, and standardize analytical workflows.

• Provide evidence of your experience utilizing AI tools or LLMs for data analysis, reporting, or process automation. Process Documentation:

• Evidence of creating clear, concise documentation for research processes, from planning and recruitment to analysis and reporting.

• Examples of documenting and standardizing data analysis workflows to improve efficiency and reproducibility.

• Documentation showcasing how you've supported or improved data pipelines, including data validation and integration steps.

• Case studies that illustrate your approach to improving process efficiency through automation or methodological refinement.

📝 Enhancement Note: For a mixed-methods role like this, the portfolio should clearly articulate the candidate's ability to bridge qualitative insights with quantitative rigor. Specifically, demonstrating how research findings informed product decisions and how data analysis led to actionable improvements or process optimizations will be crucial. The emphasis on process documentation and efficiency suggests a need to showcase not just what was done, but how it was done effectively and systematically.

💵 Compensation & Benefits

Salary Range:

• Salary Grade 6: $85,400 - $143,200 USD per year

• Salary Grade 7: $99,600 - $166,600 USD per year Benefits:

• Immediate medical, dental, vision, and prescription drug coverage.

• Flexible family care days, paid parental leave, and new parent ramp-up programs.

• Subsidized back-up child care and family-building benefits (adoption/surrogacy reimbursement, fertility treatments).

• Vehicle discount program for employees and family members, along with management leases.

• Tuition assistance for continued education and professional development.

• Access to established and active employee resource groups.

• Paid time off for individual and team community service initiatives.

• A generous schedule of paid holidays, including the week between Christmas and New Year's Day.

• Paid time off and the option to purchase additional vacation time. Working Hours:

• Standard full-time work week of approximately 40 hours.

• Hybrid work model requiring 4 or more days onsite per week in Dearborn, MI.

📝 Enhancement Note: The provided salary ranges for Grades 6 and 7 indicate a competitive compensation structure for mid-level to senior mid-level UX Researchers and Data Analysts in the automotive industry. The extensive benefits package reflects Ford's commitment to employee well-being, work-life balance, and professional growth, which are attractive to operations professionals who value comprehensive support.

🎯 Team & Company Context

🏢 Company Culture

Industry: Automotive Manufacturing & Technology

Company Size: Large Enterprise (Over 10,000 employees)

Founded: 1903

Company History: Ford Motor Company, a global leader in the automotive industry, has a rich history of innovation and has consistently adapted to evolving technological landscapes and consumer needs. This legacy of transformation is now being applied to digital product design and customer experience, aiming to shape the future of mobility.

Team Structure:

• The Digital Product Design (DPD) team is a multidisciplinary group comprising Product Designers, Researchers, Visual Designers, Industrial Designers, Modelers, and Project Managers.

• This role specifically supports the Customer Confidence Center of Excellence within DPD.

• Collaboration is expected to be high, with close partnerships between design, product, and engineering teams. Methodology:

• Emphasis on data-driven decision-making, leveraging both qualitative research and quantitative data analytics.

• Application of rigorous research methodologies to inform design strategy and product development.

• Focus on continuous improvement of features and user experiences through actionable insights.

• Increasing adoption of AI and LLM tools for enhanced efficiency and analysis.

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

📝 Enhancement Note: Ford's established presence and commitment to future mobility indicate a dynamic environment where operations professionals can contribute to significant, large-scale projects. The integration of digital product design within a traditional manufacturing giant suggests a blend of established processes and innovative, forward-thinking initiatives, particularly relevant for roles focused on customer experience and data analysis.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a mid-level to senior mid-level contributor (Salary Grades 6-7), requiring a solid foundation in both UX research and data analysis. It involves executing research plans, supporting large-scale programs, and contributing to process optimization.

Reporting Structure: The role reports into the Digital Product Design (DPD) team, likely within a management structure overseeing UX research or customer insights. Collaboration will be extensive with cross-functional teams, including product management, engineering, and potentially marketing or customer service.

Operations Impact: The primary impact of this role is on enhancing customer confidence and satisfaction with Ford's digital products and vehicle experiences. By providing clear, data-backed insights, this position directly influences design decisions, feature development, and ultimately, customer loyalty and brand perception. The focus on process efficiency also contributes to operational cost savings and faster product iteration cycles.

Growth Opportunities:

• Specialization: Deepen expertise in specific research methodologies, advanced statistical analysis, or AI-driven data insights within the automotive context.

• Leadership: Progress to a Senior UX Researcher or Data Analyst role, potentially leading research initiatives or mentoring junior team members.

• Cross-functional Mobility: Opportunity to move into product management, customer experience strategy, or analytics leadership roles within Ford.

• Skill Development: Continuous learning through exposure to cutting-edge automotive technology, evolving UX research tools, and advanced data analytics techniques.

📝 Enhancement Note: The dual salary grades suggest flexibility in hiring based on candidate experience and potential, offering a clear path for advancement. The emphasis on process improvement and AI tools positions this role at the forefront of modern operations and analytics within a large enterprise, providing a strong foundation for future career growth in data-informed product development.

🌐 Work Environment

Office Type: This is a hybrid role, requiring a significant portion of the work week to be conducted onsite at Ford's Dearborn, MI campus. The environment is likely a modern office setting designed for collaboration.

Office Location(s): Dearborn, Michigan, USA. This is Ford's global headquarters, offering access to extensive resources, facilities, and a large employee base.

Workspace Context:

• Collaborative Environment: Expect to work closely with a diverse team of designers, researchers, product managers, and engineers, fostering a dynamic and interactive workspace.

• Tools & Technology: Access to industry-standard design and research tools, as well as robust data analytics platforms and computing resources necessary for complex analysis.

• Team Interaction: Opportunities for regular in-person meetings, design critiques, brainstorming sessions, and cross-functional project reviews, vital for a mixed-methods role.

Work Schedule: The role is full-time, approximately 40 hours per week. The hybrid model necessitates being onsite at least four days per week, allowing for dedicated time for in-person collaboration and access to office resources, while still offering some flexibility.

📝 Enhancement Note: The requirement for 4+ days onsite per week in Dearborn underscores the importance of in-person collaboration for this role. This suggests that a significant part of the job involves direct interaction, team brainstorming, and leveraging shared resources, which is common in product development environments focused on iterative design and rapid problem-solving.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and portfolio to assess qualifications and alignment with the role requirements.

• Hiring Manager Interview: Discussion about your experience, research approach, data analysis skills, and understanding of customer-centered design.

• Technical/Skills Assessment: This may involve a case study, a presentation of your portfolio, or a discussion around specific research or data analysis challenges you've encountered. Be prepared to walk through your process, methodologies, and the impact of your work.

• Team/Cross-functional Interviews: Meetings with potential colleagues and stakeholders from design, product, and engineering teams to evaluate collaboration style and cultural fit.

• Final Interview: Potentially with senior leadership to discuss strategic alignment and overall fit within the organization.

Portfolio Review Tips:

• Mixed-Methods Showcase: Clearly present projects that demonstrate both qualitative UX research expertise and quantitative data analysis capabilities.

• Process & Impact: For each project, detail your role, the problem statement, your methodology, the insights generated, and crucially, the impact of your work on product decisions or business outcomes. Quantify impact whenever possible.

• Data Analysis Clarity: If showcasing data analysis, explain your approach to data cleaning, analysis techniques, and how you visualized and communicated findings. Highlight any process improvements you implemented.

• AI/LLM Integration: If applicable, include projects where you utilized AI or LLM tools for analysis, reporting, or automation, explaining the benefits and outcomes.

• Conciseness & Storytelling: Present your work in a clear, engaging narrative. Focus on the most relevant and impactful projects.

Challenge Preparation:

• Research Design: Be ready to discuss how you would approach a research problem for a specific Ford digital product or feature.

• Data Interpretation: Prepare to analyze a sample dataset or interpret customer feedback to derive actionable insights.

• Process Optimization: Think about how you would improve a given process (e.g., survey data collection, research reporting) for efficiency.

• Stakeholder Communication: Practice articulating complex findings and recommendations clearly and persuasively to different audiences.

📝 Enhancement Note: Given the mixed-methods nature of the role, the portfolio review will likely be a critical component. Candidates should prepare to articulate their reasoning behind choosing specific research and analysis methods and demonstrate how these methods led to tangible improvements. The emphasis on process improvement suggests that interviewers will be looking for candidates who can not only execute tasks but also optimize the systems they work within.

🛠 Tools & Technology Stack

Primary Tools:

• UX Research Platforms: Figma (for design collaboration/prototyping), Protopie (for advanced prototyping), Dscout (for qualitative data collection), Qualtrics (for survey design and fielding), UserTesting.com (for remote usability testing).

• Data Analysis & Visualization: Advanced MS Excel (including VBA), Power BI, Tableau, Looker Studio.

• Data Wrangling & Integration: SQL (for querying), R programming (for statistical analysis).

• Collaboration & Project Management: Jira (for Agile processes), Microsoft Office Suite (Word, PowerPoint, Outlook).

• AI & Automation: Experience with AI tools for secondary data analysis, unstructured feedback, data presentation, and process automation is a requirement. Specific tools may vary but familiarity with LLM applications in data is key.

Analytics & Reporting:

• Tools for tracking performance to KPIs, competitive benchmarking, and deep-dive reporting.

• VOC dashboard updates and potential creation/maintenance. CRM & Automation:

• While not explicitly listed as primary CRM, understanding how customer feedback integrates with CRM data for a holistic view is beneficial.

• Automation tools for workflow design and efficiency management, particularly in data processing and reporting.

📝 Enhancement Note: The inclusion of specific tools like Figma, Qualtrics, Power BI, Tableau, and SQL indicates a need for hands-on experience with modern UX research and data analytics stacks. Proficiency in these tools, combined with a strong understanding of statistical analysis and AI applications, will be highly valued.

👥 Team Culture & Values

Operations Values:

• Customer-Centricity: A deep commitment to understanding and advocating for the customer throughout the design and development process.

• Data-Driven Decision-Making: Valuing evidence and insights derived from research and analytics to guide strategy and execution.

• Continuous Improvement: A proactive approach to refining processes, tools, and product experiences for greater efficiency and effectiveness.

• Collaboration & Teamwork: Fostering an environment where diverse perspectives are welcomed, and cross-functional partnerships thrive.

• Innovation: Embracing new technologies and methodologies, including AI and LLMs, to push the boundaries of what's possible in automotive digital experiences.

Collaboration Style:

• Cross-functional Integration: Expect to work closely with product managers, engineers, and other designers, requiring strong communication and negotiation skills to align on research goals and translate findings into actionable plans.

• Feedback-Rich Environment: A culture that encourages constructive feedback on research plans, methodologies, and insights to ensure rigor and impact.

• Knowledge Sharing: Opportunities to share learnings from research and data analysis across teams, contributing to a collective understanding of the customer.

📝 Enhancement Note: Ford's emphasis on transforming the future suggests a culture that values forward-thinking and adaptability. For an operations-focused role, this translates to embracing new tools and methodologies, driving efficiency, and ensuring that customer insights are a cornerstone of business strategy.

⚡ Challenges & Growth Opportunities

Challenges:

• Balancing Research & Analysis: Effectively managing time and resources between hands-on qualitative research execution and in-depth quantitative data analysis and reporting.

• Data Volume & Complexity: Navigating large datasets from various sources (surveys, product usage, feedback) to extract meaningful and actionable insights.

• Cross-functional Alignment: Ensuring research findings and data insights are understood and acted upon by diverse stakeholders with varying priorities.

• Evolving Technology: Staying abreast of rapid advancements in AI, LLMs, and UX research methodologies and applying them effectively within the automotive context.

• Pace of Automotive Development: Adapting to the fast-paced nature of vehicle program development and ensuring research and analysis timelines meet project deadlines.

Learning & Development Opportunities:

• Advanced Analytics Training: Opportunities to deepen expertise in statistical modeling, machine learning, and data visualization tools.

• UX Research Specialization: Pursuing advanced training or certifications in specific qualitative or quantitative research techniques.

• AI/LLM Application: Gaining hands-on experience and training in applying AI and LLM tools for enhanced data analysis and process automation within Ford.

• Automotive Industry Insights: Developing a deep understanding of the automotive market, vehicle technology, and evolving customer expectations.

• Mentorship & Career Pathing: Access to mentorship programs and clear career progression paths within Ford's extensive operations and design organizations.

📝 Enhancement Note: The challenges presented are typical for a mixed-methods role in a large, complex industry. Ford's commitment to growth and development suggests that employees will have ample opportunities to build skills in areas like AI, advanced analytics, and specialized research, which are highly valuable in today's job market.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used mixed methods to uncover a critical user insight that led to a significant product change. What was your process, and what was the outcome?" (Focus on methodology, insight generation, and demonstrable impact.)

• "How would you approach analyzing unstructured customer feedback from surveys to identify emerging trends or pain points for Ford's in-car infotainment system?" (Demonstrate analytical approach, tool usage, and ability to derive actionable insights.)

• "Imagine we need to improve the efficiency of our customer survey data processing. What steps would you take, and what tools or technologies (e.g., AI, automation) might you leverage?" (Highlight process improvement mindset, technical skills, and innovation.) Company & Culture Questions:

• "What do you know about Ford's digital product strategy or recent innovations in customer experience?" (Show research into the company and its direction.)

• "How do you see your role contributing to Ford's mission of transforming the future of mobility?" (Connect your skills to the company's broader goals.)

• "Describe a time you had to present complex data or research findings to a non-technical audience, such as senior leadership. How did you ensure they understood and acted on your recommendations?" (Assess communication and stakeholder management skills.) Portfolio Presentation Strategy:

• Structure: Organize your portfolio by project type (e.g., UX Research, Data Analysis, Process Improvement) or by impact. Use a consistent template for each case study.

• Narrative: For each project, clearly articulate the problem, your role, the methodology, key findings, recommendations, and the measurable impact.

• Visuals: Use screenshots, charts, graphs, and other visuals to illustrate your work and findings effectively. For data analysis, ensure visualizations are clear and compelling.

• Tool Demonstration: Be prepared to briefly demonstrate or discuss your proficiency with key tools relevant to the role (e.g., Qualtrics, Tableau, Excel, AI tools).

• Conciseness: Focus on 2-3 of your most impactful and relevant projects. Be ready to go deeper on any aspect of your work.

📝 Enhancement Note: Interviewers will likely probe your ability to connect research and data analysis to tangible business outcomes. Prepare to discuss your thought process for choosing methodologies, your approach to data integrity, and how you translate findings into actionable recommendations that align with Ford's strategic objectives.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided Oracle Cloud portal link.

• Curate Your Portfolio: Select 2-3 of your most relevant projects that showcase both your mixed-methods UX research and data analysis skills. Tailor your portfolio to highlight experience with customer feedback, process efficiency, and AI/LLM tools.

• Optimize Your Resume: Ensure your resume clearly articulates your years of experience in UX research and data analysis, details your proficiency with required tools (Excel, AI tools, Qualtrics, SQL, etc.), and quantifies achievements where possible, especially those related to process improvement or user experience enhancement.

• Prepare Your Presentation: Practice presenting your portfolio, focusing on clear storytelling, explaining your methodology, and articulating the impact of your work. Be ready to discuss how you would approach a hypothetical research or data analysis challenge relevant to Ford.

• Research Ford's Digital Presence: Familiarize yourself with Ford's current digital products, customer experience initiatives, and any recent news related to their digital transformation or mobility strategy. This will help you tailor your responses and demonstrate genuine interest.

⚠️ 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.