UX Researcher, Mixed Methods

📍 Job Overview

Job Title: UX Researcher, Mixed Methods

Company: Ford

Location: Dearborn, Michigan, United States

Job Type: FULL_TIME

Category: User Experience (UX) Research / Data Analysis

Date Posted: September 09, 2026

Experience Level: 2-5 years

Remote Status: Hybrid (4+ days onsite per week)

🚀 Role Summary

• This role is pivotal for enhancing user experiences within Ford's Digital Product Design (DPD) team, specifically supporting the Customer Confidence Center of Excellence.

• It requires a blend of rigorous user research execution and comprehensive data analysis to drive evidence-based design decisions for automotive digital products.

• The position involves managing and supporting the Voice of the Customer (VOC) program, analyzing survey data, and identifying opportunities for process efficiency improvements.

• Success in this role will be measured by the ability to translate complex research findings and customer feedback into clear, actionable insights that directly influence product development and customer satisfaction.

📝 Enhancement Note: While the primary title is UX Researcher, the inclusion of "Mixed Methods," "Data Analyst," and explicit responsibilities around "Voice of the Customer Program Support," "data analysis and reporting," and "process efficiency" indicate a strong analytical and operational component to this UX role, requiring candidates to demonstrate not only research acumen but also data management and process improvement skills. The salary range provided suggests a mid-level position.

📈 Primary Responsibilities

• Plan, conduct, moderate, and analyze a variety of qualitative and quantitative user research activities, including usability studies, heuristic evaluations, concept testing, and usability benchmarking.

• Collaborate closely with design, product, and engineering teams to define research objectives and methodologies that align with product goals and customer needs.

• Analyze research findings from various sources (qualitative studies, surveys, telemetry) and synthesize them into clear, actionable insights and compelling presentations for stakeholders.

• Support the management of the Voice of the Customer (VOC) program, including assisting with supplier management, program planning, survey roadmap coordination, sample procurement, and budget administration.

• Conduct ongoing customer and vehicle reporting, track performance against Key Performance Indicators (KPIs), perform competitive benchmarking, and deliver deep-dive analysis reports.

• Investigate and assist in managing data pipelines, support data integration initiatives, and contribute to updates and training for VOC dashboards.

• Drive improvements in data analysis process efficiency by documenting, standardizing, and formalizing processes, leveraging integrated data sources, and utilizing AI tools like LLMs for faster analysis and automation.

• Present research findings and data insights effectively to senior management and cross-functional teams, ensuring understanding and buy-in for design recommendations.

• Actively participate in Agile development processes, contributing research and data insights to iterative product development cycles.

📝 Enhancement Note: The responsibilities highlight a dual focus: direct UX research execution and indirect VOC program management/data analysis. The emphasis on process improvement using AI and integrated data sources points to a need for operational efficiency within the research function.

🎓 Skills & Qualifications

Education:

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

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

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

• Advanced proficiency in Microsoft Excel (pivot tables, advanced formulas, data manipulation), PowerPoint (presentation design and delivery), Word, and general communication tools.

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

• Solid understanding of quantitative and qualitative research methods, including sampling strategies, questionnaire design, data capture, fielding methodologies, and reporting.

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

• Ability to present effectively to senior management and cross-functional teams, articulating complex information clearly.

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

• Self-starter with the ability to manage multiple tasks and projects with minimal direction.

• Strong customer empathy and problem-solving skills.

• Excellent teamwork, interpersonal, and communication skills.

• Strong organizational skills with meticulous attention to detail. Preferred Skills:

• Degree in Engineering, Human Factors, UX, Human-Computer Interaction, Industrial Design, or a related field.

• Technical acumen for setting up and troubleshooting functional prototypes.

• Experience with Agile development processes and tools (e.g., Jira).

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

• Proficiency with SQL for data extraction and manipulation.

• Experience with advanced statistical modeling techniques (e.g., regression, clustering, machine learning).

• Experience with data visualization tools like Power BI, Tableau, Looker Studio, or similar.

• Experience with data wrangling, including transformation, cleaning, and validation for analysis-ready datasets.

• Experience with R programming for statistical analysis.

• Previous experience in the automotive industry.

📝 Enhancement Note: The requirements clearly delineate between essential skills and preferred additions, providing a strong guide for candidates. The emphasis on AI tools for analysis and automation is a key differentiator, reflecting modern trends in operations and research. The experience level of 1-3 years in both research and data analysis, coupled with the salary range, positions this as a strong mid-level role.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies: Showcase at least 2-3 detailed case studies demonstrating experience in both qualitative UX research and quantitative data analysis, with a clear emphasis on mixed-methods approaches.

• Problem-Solution-Impact: Each case study should clearly outline the problem statement, the mixed-methods research and analysis approach employed, the key insights generated, and the tangible impact or improvements achieved (e.g., improved usability, increased customer satisfaction, efficiency gains).

• Data Visualization: Include examples of how you've visualized complex data sets and research findings to make them easily understandable for diverse audiences, including non-technical stakeholders.

• Process Improvement Examples: Highlight instances where you've identified inefficiencies in research or data analysis processes and implemented solutions for improvement, ideally showcasing the use of tools or automation.

• Tools & Technologies: Briefly mention the specific tools and technologies used within your portfolio projects (e.g., Qualtrics, Figma, SQL, Excel, Power BI, AI tools).

Process Documentation:

• Candidates are expected to demonstrate an understanding of process standardization and documentation, particularly in the context of research operations and data analysis workflows.

• Experience in documenting research plans, analysis methodologies, and reporting templates is valuable.

• The ability to formalize and standardize processes for efficiency, as mentioned in the job description ("Improve data analysis process efficiency by documenting, standardizing, and formalizing processes"), should be evident in portfolio examples or discussed during interviews.

📝 Enhancement Note: For a role combining UX Research and Data Analysis with an operational component, a portfolio is crucial. The emphasis on mixed-methods, data visualization, and process improvement suggests that candidates should prepare to showcase their ability to connect research insights to business outcomes and operational efficiency.

💵 Compensation & Benefits

Salary Range:

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

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

• The final determination of salary grade will be based on the candidate's skills, experience, the job's scope, responsibilities, and competitive market value. Benefits:

• Immediate comprehensive 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 services.

• Family building benefits, including reimbursement for adoption and surrogacy expenses, and fertility treatments.

• Employee vehicle discount program for employees and family members, plus management leases.

• Tuition assistance programs for continued education.

• Access to established and active employee resource groups (ERGs).

• 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 (PTO) with the option to purchase additional vacation time. Working Hours:

• Standard full-time position, likely around 40 hours per week. The role is hybrid, requiring the employee to be onsite at the Dearborn, MI location four or more days per week.

📝 Enhancement Note: The salary ranges provided are specific and cover two potential grades, indicating flexibility based on candidate qualifications. The benefits package is extensive, covering health, family support, professional development, and work-life balance, which are attractive to operations and research professionals. The hybrid requirement is clearly stated.

🎯 Team & Company Context

🏢 Company Culture

Industry: Automotive Manufacturing and Technology. Ford is a global leader in the automotive industry, currently undergoing significant transformation towards electric vehicles, connected services, and advanced digital experiences. This context means the Digital Product Design (DPD) team operates at the forefront of innovation within a large, established corporation.

Company Size: Ford Motor Company is a large enterprise, employing tens of thousands of individuals globally. This scale offers opportunities for broad impact, access to extensive resources, and diverse career paths, but also requires navigating complex organizational structures.

Founded: Ford Motor Company was founded by Henry Ford in 1903. This long history signifies a deep-rooted engineering and manufacturing legacy, now being augmented by a strong focus on digital transformation and customer experience.

Team Structure:

• The Digital Product Design (DPD) team is a specialized unit within Ford, dedicated to creating digital product experiences. It comprises various roles including Product Designers, Researchers, Visual Designers, Industrial Designers, Modelers, and Project Managers.

• The UX Researcher will be part of the Customer Confidence Center of Excellence within DPD. This suggests a focus on areas related to customer satisfaction, trust, and post-purchase experience within the digital ecosystem.

• Collaboration is expected to be cross-functional, working closely with product managers, engineers, data scientists, and potentially marketing and customer service teams. Methodology:

• The DPD team emphasizes a user-centered design approach, striving for "simple, thoughtful, enjoyable, and compelling product experiences."

• Research and analysis are key to informing design decisions, with a strong reliance on both qualitative and quantitative data.

• The role involves supporting Agile processes, indicating an iterative and collaborative development methodology.

• A focus on data analysis, process efficiency, and leveraging AI tools suggests a forward-thinking, data-driven operational approach within the design function.

Company Website: ford.com

📝 Enhancement Note: Understanding Ford's position as a legacy automaker embracing digital transformation is crucial. The DPD team's role in this shift, especially within the Customer Confidence Center of Excellence, implies a focus on enhancing user trust and satisfaction with digital touchpoints in the automotive journey.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a mid-level UX Researcher with a significant data analysis and operational component. It requires 1-3 years of dedicated experience in both UX research and data analysis. The opportunity to be placed in Salary Grade 6 or 7 indicates potential for advancement within the role or to more senior research/analytical positions based on performance and acquired skills.

Reporting Structure: The UX Researcher will report within the Digital Product Design (DPD) organization, specifically supporting the Customer Confidence Center of Excellence. While the direct manager isn't specified, it's likely a UX Research Lead, Design Manager, or a similar senior role within DPD. The role will require collaboration with various product teams and stakeholders across the organization.

Operations Impact: The impact of this role is directly tied to improving customer experiences with Ford's digital products. By providing rigorous research insights and actionable data analysis, the UX Researcher will influence design decisions that can lead to increased customer satisfaction, higher adoption rates of digital features, enhanced brand loyalty, and potentially reduced customer support costs. The focus on VOC program support also directly links to customer sentiment and business performance metrics.

Growth Opportunities:

• Skill Specialization: Deepen expertise in mixed-methods research, advanced statistical analysis, AI-driven data analysis, and specific automotive digital product domains.

• Leadership Development: Progress to Senior UX Researcher or Lead Researcher roles, mentoring junior team members, and leading larger research initiatives. Opportunity to transition into a dedicated Data Analyst role or a UX Program Manager role focusing on research operations.

• Cross-Functional Exposure: Gain broader experience by working on various vehicle programs and digital platforms, potentially moving into product management or strategy roles.

• Process Improvement Leadership: Take ownership of refining research operations and data analysis workflows, leveraging new technologies and methodologies to drive efficiency and impact.

• Industry Trends: Stay at the cutting edge of UX research and data analytics in the automotive and technology sectors, contributing to Ford's innovation.

📝 Enhancement Note: This role offers a unique hybrid path, allowing for growth in both traditional UX research and data analytics/operations. The dual focus provides flexibility for career development, whether leaning more into research leadership or data-driven operational excellence.

🌐 Work Environment

Office Type: This is a hybrid role, meaning a blend of remote and in-office work. The requirement is to be onsite four or more days per week in Dearborn, Michigan. This suggests a collaborative office environment designed to foster teamwork and in-person interaction.

Office Location(s): Dearborn, Michigan, USA. This is Ford's historical headquarters and a major hub for its operations and R&D.

Workspace Context:

• Collaborative Environment: The expectation of being onsite four days a week indicates a focus on in-person collaboration, team meetings, workshops, and brainstorming sessions. The DPD team likely has dedicated spaces for design and research activities.

• Tools and Technology: Access to standard office equipment, high-speed internet, and likely a robust IT infrastructure supporting research tools, design software, and data analysis platforms. The job description mentions specific tools like Figma, Qualtrics, SQL, and various analytics/visualization platforms.

• Team Interaction: Opportunities to interact daily with fellow UX researchers, designers, product managers, engineers, and data analysts, fostering a rich learning and collaborative ecosystem.

Work Schedule:

• Standard full-time hours, approximately 40 hours per week. The hybrid model provides some flexibility, but the significant onsite requirement indicates a need for structured workdays centered around office presence for collaboration and team engagement.

📝 Enhancement Note: The hybrid nature with a strong onsite component is a key characteristic. For operations roles, this often means a structured work environment that balances focused individual work with essential team collaboration and knowledge sharing.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or a recruiter will likely conduct an initial phone screen to assess basic qualifications, salary expectations, and cultural fit.

• Hiring Manager Interview: A conversation with the hiring manager to dive deeper into your experience, motivations, and understanding of the role.

• Technical/Skills Assessment: This may involve a take-home assignment or a live exercise focusing on research design, data analysis, or presenting findings. This is where your portfolio will be critical.

• Portfolio Review Session: A dedicated session where you will present 1-2 key case studies from your portfolio to the hiring team. Be prepared to discuss your process, insights, impact, and how you handled challenges.

• Team/Cross-functional Interviews: Interviews with other members of the DPD team or stakeholders you'd be collaborating with. These sessions often assess teamwork, communication, and problem-solving skills.

• Final Interview: Potentially with a senior leader or director to finalize the decision.

Portfolio Review Tips:

• Curate Strategically: Select 2-3 projects that best showcase your mixed-methods research skills, data analysis capabilities, and ability to derive actionable insights. Ensure at least one project highlights process improvement or data integration.

• Structure for Impact: For each case study, clearly articulate the problem, your role, the methodology (research and analysis), key findings, design recommendations, and most importantly, the measurable impact or outcome. Use visuals effectively.

• Highlight Operations Aspects: Emphasize any work where you improved research efficiency, managed data pipelines, standardized processes, or leveraged AI/automation for analysis.

• Be Ready to Discuss: Prepare to answer detailed questions about your decision-making process, challenges faced, how you handled ambiguity, and how your work directly influenced product decisions or business outcomes.

• Tailor to Ford: If possible, subtly tailor your examples to automotive or complex digital product contexts, demonstrating an understanding of the industry.

Challenge Preparation:

• Research Design: Be prepared to design a research study (qualitative, quantitative, or mixed-methods) for a given Ford product or feature.

• Data Analysis & Interpretation: You might be given a dataset or survey results and asked to analyze them, identify key trends, and present actionable insights.

• Problem-Solving: Expect questions that test your ability to break down complex user problems and propose research-driven solutions.

• Process Improvement Scenarios: Be ready to discuss how you would approach improving a research or data analysis process, drawing on your experience with AI and automation.

📝 Enhancement Note: The interview process for a role like this will heavily scrutinize both research methodology and analytical rigor. A well-prepared portfolio that clearly demonstrates mixed-methods expertise, data analysis skills, and a proactive approach to process improvement will be essential.

🛠 Tools & Technology Stack

Primary Tools:

• Research Platforms: Qualtrics, UserTesting.com, Dscout, or similar platforms for survey deployment, usability testing, and qualitative data collection.

• Design & Prototyping Tools: Figma, Protopie (for research support, understanding prototypes, and collaborating with designers).

• AI Tools: Explicitly mentioned for secondary data analysis, unstructured feedback analysis, data presentation, and process automation. This could include LLMs (e.g., ChatGPT, Bard) or specialized AI analytics platforms.

Analytics & Reporting:

• Spreadsheet Software: Advanced proficiency in MS Excel for data manipulation, analysis, and reporting.

• Data Visualization Tools: Power BI, Tableau, Looker Studio for creating dashboards and communicating insights effectively.

• Statistical Software/Languages: Familiarity with statistical analysis concepts, with preferred experience in R programming or SQL for data wrangling and analysis.

CRM & Automation:

• CRM Systems: While not explicitly mentioned, understanding how research and VOC data tie into CRM systems (e.g., Salesforce) for customer insights would be beneficial.

• Project Management Tools: Jira (preferred) for participating in Agile development cycles.

• Data Integration Tools: Experience or understanding of how data pipelines are managed and integrated is a plus.

📝 Enhancement Note: The technology stack is quite broad, reflecting the mixed-methods and data-intensive nature of the role. Candidates should be prepared to discuss their experience with a range of tools, with a particular emphasis on AI applications for analysis and process efficiency.

👥 Team Culture & Values

Operations Values:

• Customer Centricity: A strong emphasis on understanding and advocating for the customer, ensuring their needs and pain points are central to design and product decisions.

• Data-Driven Decision Making: A commitment to using research findings and data analytics to inform strategy and validate design choices, moving beyond intuition.

• Efficiency & Continuous Improvement: A proactive approach to optimizing processes, leveraging technology (including AI) to work smarter and deliver insights faster.

• Collaboration & Teamwork: A culture that values cross-functional partnerships, open communication, and collective problem-solving to achieve common goals.

• Innovation & Future-Forward Thinking: An environment that encourages exploration of new technologies, methodologies, and product concepts to shape the future of mobility.

Collaboration Style:

• Cross-Functional Integration: Researchers and analysts are expected to work closely with product managers, designers, engineers, and other stakeholders, acting as a bridge between customer needs and product development.

• Process Review & Feedback: An open culture for sharing research plans, analysis methodologies, and findings, welcoming constructive feedback to refine approaches and ensure clarity.

• Knowledge Sharing: Encouragement of sharing insights, best practices, and learnings across the team and with wider product development groups, fostering a learning organization.

📝 Enhancement Note: The values align with a modern, agile, and data-informed product development environment. For operations professionals, the emphasis on efficiency and data-driven decision-making is particularly relevant.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating a Large Organization: Adapting to Ford's corporate structure, understanding stakeholder needs across different departments, and ensuring research insights gain traction and influence.

• Balancing Research Depth with Speed: The need to conduct rigorous research while also supporting fast-paced product development cycles and urgent requests for data.

• Data Integration Complexity: Working with potentially disparate data sources and systems to create cohesive insights, requiring strong data wrangling and integration skills.

• Evolving Automotive Landscape: Staying abreast of rapid advancements in automotive technology, connected services, and user expectations in a highly competitive market.

• Measuring Impact: Quantifying the direct impact of research and data analysis on product success and business outcomes can be challenging but is critical for demonstrating value.

Learning & Development Opportunities:

• Operations Skill Advancement: Opportunities to specialize further in areas like research operations, data science, statistical modeling, or AI applications for research.

• Industry Exposure: Participation in industry conferences, workshops, and training sessions focused on UX research, data analytics, and automotive technology.

• Mentorship & Leadership: Potential for mentorship from senior researchers or leaders within DPD, and opportunities to grow into leadership roles managing research projects or teams.

• Cross-Functional Projects: Gaining exposure to different product areas and business units within Ford, broadening understanding of the entire automotive value chain.

📝 Enhancement Note: This role presents a valuable opportunity for growth by tackling complex challenges within a large, innovative company and developing a versatile skill set at the intersection of UX, data, and operations.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you used mixed-methods research to solve a complex user problem. What were the key challenges, and how did you overcome them?" (Focus on methodology, insights, and impact.)

• "How would you design a research study to understand customer confidence in a new autonomous driving feature for Ford?" (Demonstrate research planning, consideration of methods, and stakeholder alignment.)

• "Walk us through a process you've improved in your previous roles related to data analysis or research workflow. What tools did you use, and what was the outcome?" (Highlight efficiency, automation, and operational thinking.) Company & Culture Questions:

• "What interests you about Ford's approach to digital product design and the automotive industry's future?" (Show research into Ford's strategy and your passion for the domain.)

• "How do you ensure your research and data insights are actionable and understood by non-technical stakeholders?" (Focus on communication, presentation skills, and impact.)

• "Describe your experience working in a hybrid environment and how you maintain collaboration and productivity." (Address the work arrangement and team dynamics.) Portfolio Presentation Strategy:

• Structure Your Narrative: For each case study, use a clear story arc: Problem -> Your Role/Approach -> Insights -> Recommendations -> Impact/Outcome.

• Quantify Whenever Possible: Use metrics to demonstrate the impact of your work. If direct metrics are unavailable, discuss how your work influenced decisions that could lead to measurable outcomes.

• Highlight Mixed-Methods: Clearly articulate how combining qualitative and quantitative data provided a more complete understanding than either method alone.

• Showcase Process Improvement: If you have an example of improving a research or data analysis process, make sure to highlight it, especially if AI or automation was involved.

• Prepare for Deep Dives: Be ready to answer detailed questions about your methodology, tool choices, statistical approaches, and how you handled any limitations or unexpected findings.

📝 Enhancement Note: Candidates should prepare to showcase not just research skills, but also analytical prowess and operational efficiency. The interview process will likely assess how well they can connect user needs to business objectives and how they can streamline the research and analysis process.

📌 Application Steps

To apply for this operations-adjacent UX Research position:

• Submit your application through the Ford careers portal linked in the job posting.

• Portfolio Customization: Tailor your resume and cover letter to highlight your experience in mixed-methods research, data analysis, process improvement, and AI tool utilization, using keywords from the job description.

• Portfolio Preparation: Select 2-3 strong case studies that showcase your ability to conduct research, analyze data, derive insights, and demonstrate impact, with a focus on any process efficiencies you've driven. Be ready to present these clearly and concisely.

• Interview Practice: Practice articulating your research process, data analysis techniques, and problem-solving approaches. Prepare answers to common UX research and data analysis interview questions, and rehearse your portfolio presentation.

• Company Research: Familiarize yourself with Ford's current digital initiatives, its approach to customer experience, and its position in the evolving automotive market. Understand their commitment to innovation and data-driven design.

⚠️ 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.