Senior Product Designer

📍 Job Overview

Job Title: Senior Product Designer

Company: Ford Motor Company

Location: Dearborn, Michigan, United States

Job Type: Full time

Category: Product Design / Data & Analytics / Technology

Date Posted: April 02, 2026

Experience Level: 5-10 Years

Remote Status: Hybrid (4 days onsite)

🚀 Role Summary

• Drive the end-to-end product design lifecycle for innovative AI-driven insight and analytics products within Ford's Global Data Insights & Analytics (GDI&A) department.

• Leverage user experience principles and design thinking to ensure AI methods and intelligent systems effectively serve their end-users, focusing on human-centric roadmaps.

• Collaborate closely with product managers, data scientists, AI engineers, and fellow designers to define and execute the future of AI-powered data experiences and agentic workflows.

• Oversee a portfolio of product teams, identifying cross-portfolio patterns, use cases, and opportunities to enhance user value and drive evidence-based decision-making.

📝 Enhancement Note: This role sits within the Global Data Insights & Analytics (GDI&A) department, specifically in the data science division. The emphasis on "AI-driven Insight and Analytics products," "intelligent systems," "agentic workflows," and "dynamic data experiences" indicates a focus on advanced, data-centric product design, moving beyond traditional UI/UX to design sophisticated, AI-powered tools. The "Senior" title suggests a need for leadership, strategic thinking, and experience managing complex projects or portfolios.

📈 Primary Responsibilities

• Define and execute the product design strategy and roadmap for AI-driven insight and analytics platforms, ensuring alignment with business objectives and user needs.

• Conduct comprehensive user research, including interviews, surveys, and usability testing, to deeply understand user pain points and opportunities for AI-enhanced data analysis.

• Translate complex data science concepts and AI capabilities into intuitive and actionable user interfaces and experiences for both internal and external stakeholders.

• Develop user flows, wireframes, interactive prototypes, and high-fidelity visual designs for AI-powered analytical tools, dashboards, and agentic workflows.

• Champion human-centric design principles across a portfolio of product teams, ensuring that AI solutions are not only technically advanced but also accessible, usable, and valuable.

• Collaborate with data scientists and AI engineers to understand model capabilities, limitations, and potential applications for user-facing products.

• Create compelling multimedia storytelling assets to communicate design rationale, user insights, and the value proposition of AI-driven analytics to diverse stakeholders, including leadership.

• Facilitate design thinking workshops and brainstorming sessions to foster innovation and drive solutions for complex data and AI challenges.

• Provide technical guidance and mentorship to junior designers, promoting best practices in AI product design and user experience.

• Iterate on designs based on user feedback, performance metrics, and evolving business requirements within an Agile development environment.

📝 Enhancement Note: The responsibilities highlight a blend of traditional product design skills with specialized knowledge in AI, data analytics, and complex system design. The emphasis on "portfolio of product teams" and "multimedia storytelling" suggests a senior role requiring strategic oversight and strong communication capabilities beyond standard design documentation.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Human-Computer Interaction (HCI), Design, Computer Science, Psychology, or a related field.

Experience:

• Minimum of 7 years of progressive experience in User Experience (UX) research and interaction design.

• Minimum of 5 years of experience in visual design and User Interface (UI) design, with a strong portfolio showcasing modern design principles.

• Proven experience in data visualization, translating complex datasets into understandable and actionable insights.

• Demonstrated experience designing and delivering complex digital products, preferably in B2B applications, AI, or data analytics domains.

Required Skills:

• Expertise in end-to-end product design, from research and ideation through to high-fidelity design and implementation support.

• Proficiency in user research methodologies, including qualitative and quantitative analysis, user interviews, and usability testing.

• Strong interaction design skills, with the ability to create intuitive user flows and information architecture.

• Advanced visual and UI design capabilities, with a keen eye for aesthetics, typography, and layout.

• Proficiency in data visualization techniques and tools, understanding how to present complex data effectively.

• Technical aptitude with a strong understanding of modern AI frameworks and concepts; ability to prototype using tools that integrate with AI models.

• Proficiency in industry-standard design and prototyping tools (e.g., Figma, Sketch, Adobe Creative Suite).

• Excellent stakeholder management and communication skills, with the ability to influence and collaborate effectively across diverse teams.

• Compelling multimedia storytelling abilities to articulate design concepts, research findings, and product value.

Preferred Skills:

• Proficiency in Python for prototyping, scripting, or understanding AI model outputs.

• Experience with designing for autonomous agents, intelligent systems, or agentic workflows.

• Familiarity with concepts like Model Context Protocols.

• Experience in technical writing or creating design documentation for complex systems.

• Understanding of business strategy and how design can drive business outcomes.

• Experience with A/B testing and data-driven design iteration.

• Knowledge of the automotive industry and its related data challenges.

📝 Enhancement Note: The requirements emphasize a strong foundation in core design disciplines (UX research, interaction, visual/UI) combined with a critical need for technical proficiency in AI, data visualization, and prototyping, specifically mentioning Python and AI frameworks. The blend of "multimedia storytelling" and "stakeholder management" points to a role that requires not just design execution but also strategic communication and influence.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• A comprehensive portfolio showcasing end-to-end product design projects, with a clear emphasis on AI-driven products, data analytics platforms, or complex intelligent systems.

• Demonstrate a deep understanding of user research methodologies and how insights were translated into design solutions.

• Include detailed case studies that illustrate the problem, your design process, key decisions, and measurable outcomes or impact.

• Showcase proficiency in data visualization design, with examples of how complex data was made accessible and actionable for users.

Process Documentation:

• Examples of user flow diagrams, wireframes, and information architecture for intricate digital products.

• Visual design examples illustrating a strong grasp of UI principles, brand consistency, and aesthetic appeal.

• Documentation of user research findings and how they informed design iterations.

• Evidence of collaboration with engineering and product management teams, including how design requirements were communicated and implemented.

• Case studies that detail the application of design thinking and human-centric methodologies to solve business problems.

📝 Enhancement Note: For a Senior Product Designer role focused on AI and data analytics, the portfolio must go beyond typical UI/UX examples. It needs to demonstrate an ability to tackle complexity, translate technical concepts into user-friendly experiences, and showcase a data-driven approach to design with measurable impact. Explicitly showing work related to AI, intelligent systems, or advanced analytics will be crucial.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Health & Wellness: Comprehensive medical, dental, and vision coverage; prescription drug coverage.

• Family Support: Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up childcare, adoption expense reimbursement, surrogacy expense reimbursement, and fertility treatments.

• Employee Perks: Vehicle discount program, tuition assistance, and access to employee resource groups.

• Time Off: Generous paid time off and paid holidays.

Working Hours:

• Standard 40-hour work week.

• This is a hybrid position requiring a minimum of four days onsite presence per week in Dearborn, MI.

📝 Enhancement Note: The salary range provided is based on AI estimation for a Senior Product Designer role with 5-10 years of experience in a major automotive company location like Dearborn, MI. This range reflects the specialized skills required, including AI/data analytics focus, and the senior level of responsibility. The extensive benefits package highlights Ford's commitment to employee well-being and family support.

🎯 Team & Company Context

🏢 Company Culture

Industry: Automotive Manufacturing & Technology

Company Size: Enterprise (10,000+ employees)

Founded: 1903

Company Description: Ford Motor Company is a global leader in the automotive industry, historically known for revolutionizing mass production and now focusing on transforming the future of mobility. They are committed to innovation, sustainability, and creating vehicles that help people pursue their dreams.

Company Specialties: Automotive Design & Engineering, Vehicle Manufacturing, Mobility Solutions, Autonomous Driving Technology, Electric Vehicles, Data & Analytics.

Team Structure:

• The role is within the Global Data Insights & Analytics (GDI&A) department, specifically the Data Science Division.

• This division operates at the forefront of AI and artificial intelligence, designing intelligent systems, agentic workflows, and dynamic data experiences.

• The Senior Product Designer will oversee a portfolio of product teams, indicating a cross-functional leadership role that interfaces with multiple development squads.

Methodology:

• Data-Driven Decision Making: The GDI&A department emphasizes leveraging data, metrics, and analytics to advise leadership and drive evidence-based decisions.

• User Experience Principles: Applying UX principles to ensure AI methods and data products serve end-users effectively.

• Design Thinking & Human-Centric Design: Employing a design thinking mindset and user research to align teams around human-centric roadmaps for AI-driven products.

• Agile Development: Working within an Agile framework to iterate and deliver solutions efficiently.

• AI & Intelligent Systems Focus: Utilizing advanced AI techniques to design innovative products and workflows.

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

📝 Enhancement Note: Ford's transition into advanced mobility and data-driven services means the GDI&A department is critical. This Senior Product Designer role is positioned at the intersection of cutting-edge AI technology and user-centric design within a large, established enterprise. The culture likely balances traditional corporate structures with a push towards innovation in data science and AI.

📈 Career & Growth Analysis

Operations Career Level: Senior Individual Contributor / Design Leadership

Reporting Structure:

• Likely reports to a Director or Senior Manager within the Global Data Insights & Analytics (GDI&A) department, possibly focused on Product Design or Data Science leadership.

Operations Impact:

• The Senior Product Designer's work directly impacts the company's ability to leverage data and AI for strategic decision-making, competitive advantage, and the development of next-generation smart vehicles and mobility services.

Growth Opportunities:

• Design Leadership: Potential to move into management roles, leading larger design teams or defining design strategy for broader product areas within GDI&A or other innovation hubs at Ford.

• Specialization: Deepen expertise in AI product design, agentic workflows, or specific areas of data analytics and visualization.

• Cross-Functional Mobility: Opportunities to move into Product Management, Technical Program Management, or strategic roles leveraging design and AI expertise.

• Industry Influence: Contribute to shaping the future of AI and data-driven design within the automotive and mobility sectors.

• Continuous Learning: Access to Ford's resources for professional development, including courses, conferences, and internal training on AI, data science, and design best practices.

📝 Enhancement Note: The "Senior" title and portfolio oversight suggest a path towards design leadership or advanced specialization. The role is pivotal for Ford's data and AI strategy, offering significant impact and opportunities for professional growth within a dynamic industry.

🌐 Work Environment

Office Type: Hybrid Work Environment

Office Location(s):

Workspace Context:

• Collaborative Spaces: The onsite workdays are intended for collaboration with cross-functional teams, including product managers, data scientists, AI engineers, and other designers. Expect modern office spaces designed to facilitate teamwork and brainstorming.

• Technology & Tools: Access to Ford's internal IT infrastructure, including company-provided hardware, software licenses for design and development tools, and robust network capabilities. This will include access to AI platforms and data environments.

• Team Interaction: Opportunities for regular in-person interactions, design reviews, team meetings, and informal knowledge sharing with colleagues within the GDI&A department and broader technology groups.

Work Schedule:

• The standard work schedule is 40 hours per week. The hybrid model allows for some flexibility in managing workdays, with the expectation of consistent onsite presence for key collaborative activities.

📝 Enhancement Note: The hybrid requirement with 4 days onsite emphasizes the importance of in-person collaboration for this role, particularly for complex projects involving AI and data science. This setup aims to balance flexibility with the need for direct team interaction and access to onsite resources.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or Recruiter call to assess basic qualifications, cultural fit, and interest in the role and Ford.

• Portfolio Review & Initial Interview: A session with hiring managers and/or design leads to review your portfolio, discuss your experience, and delve into your design process and approach to AI/data products. Expect questions about your background and how it aligns with the role's requirements.

• Technical/Design Challenge: A practical exercise, potentially involving a case study or a design problem related to AI-driven analytics. This might be a take-home assignment or a live working session.

• Cross-Functional Interviews: Interviews with key collaborators such as Product Managers, Data Scientists, and AI Engineers to assess your ability to work effectively in a multidisciplinary team and communicate technical concepts.

• Final Interview: A concluding conversation with senior leadership to discuss strategic alignment, leadership potential, and overall fit within the GDI&A department and Ford.

Portfolio Review Tips:

• Curate Strategically: Select 3-4 of your strongest projects that best showcase your experience in AI product design, data visualization, complex systems, and end-to-end design. Prioritize projects with measurable impact.

• Tell a Story: For each project, clearly articulate the problem statement, your role and responsibilities, the design process (research, ideation, iteration), key challenges overcome, your design solutions, and the final outcomes (metrics, user feedback, business impact).

• Highlight AI/Data Focus: Explicitly detail your contributions to AI-driven features, data interpretation, or complex analytics solutions. Explain how you translated technical AI concepts into user-friendly interfaces.

• Showcase Technical Aptitude: Include examples of how you've used prototyping tools, potentially scripting (like Python), or interacted with data models in your design process.

• Prepare for Questions: Anticipate questions about your design decisions, how you handle ambiguity, your collaboration style, and your understanding of AI ethics and user trust.

Challenge Preparation:

• Understand the Context: If given a case study, thoroughly research Ford's current initiatives in AI, data analytics, and mobility. Understand the business context.

• Focus on Process: Demonstrate a structured approach to problem-solving, even if you don't arrive at a "perfect" solution. Show your thought process, how you'd gather information, and what assumptions you'd make.

• AI & Data Savvy: Ensure your approach incorporates considerations for data integrity, AI model limitations, user trust, and ethical implications where relevant.

• Presentation Clarity: Prepare a clear, concise presentation of your findings and recommendations, using visuals effectively. Practice your delivery to ensure you can articulate your ideas within a given timeframe.

📝 Enhancement Note: The interview process is designed to assess not only design skills but also technical depth in AI/data, strategic thinking, and collaborative capabilities. A portfolio that specifically highlights AI and data-driven design projects will be critical for success.

🛠 Tools & Technology Stack

Primary Tools:

• Design & Prototyping: Figma (highly probable standard), Sketch, Adobe Creative Suite (Illustrator, Photoshop).

• Collaboration & Project Management: Jira, Confluence, Microsoft Teams, Slack.

• AI/Data Specific Tools: Proficiency or familiarity with tools used for AI model interaction, data visualization, and potentially scripting languages.

  • Prototyping for AI: Tools that allow for simulating AI-driven interactions or data responses.
  • Data Visualization: Tableau, Power BI, D3.js (or libraries used within them), or custom visualization frameworks.

• Version Control (for design assets/prototypes): Abstract, Git (less common for pure design but possible for integrated workflows).

Analytics & Reporting:

• Experience interpreting data from analytics platforms to inform design decisions.

CRM & Automation:

• While not directly a CRM role, understanding how user data flows from various sources (potentially including CRM) into analytics platforms is beneficial.

• Awareness of automation principles within design and development workflows.

📝 Enhancement Note: The core tools will be industry-standard design software. However, the emphasis on AI and data analytics means familiarity with data visualization tools and an understanding of how to prototype for AI-driven functionalities are key differentiators. Python proficiency is called out as a plus, suggesting it might be used for scripting prototypes or understanding data science outputs.

👥 Team Culture & Values

Operations Values:

• Data-Driven Innovation: A strong belief in leveraging data and analytics as the foundation for innovation and strategic decision-making.

• Customer Centricity: A commitment to understanding and serving the needs of customers, applying user research and design thinking to create valuable experiences.

• Collaboration & Teamwork: Emphasis on cross-functional collaboration, open communication, and shared ownership of product success.

• Continuous Improvement: A culture that encourages learning, adaptation, and ongoing refinement of processes, products, and skills, particularly in the rapidly evolving fields of AI and data science.

• Integrity & Trust: Upholding high standards in data handling, AI development, and design to build trust with users and stakeholders.

Collaboration Style:

• Cross-Functional Integration: Expect a highly integrated approach where designers work hand-in-hand with product managers, data scientists, and AI engineers throughout the product development lifecycle.

• Feedback-Rich Environment: An open culture for giving and receiving constructive feedback on designs, prototypes, and strategic approaches.

• Knowledge Sharing: Active participation in design critiques, team presentations, and documentation to share insights, best practices, and learnings across the GDI&A department.

• Agile Mindset: Embracing iterative development, flexibility, and responsiveness to changing requirements and insights.

📝 Enhancement Note: Ford's GDI&A department likely values a blend of corporate discipline with a forward-thinking, innovative approach driven by data and AI. The culture will encourage deep technical understanding combined with strong user empathy and collaborative problem-solving.

⚡ Challenges & Growth Opportunities

Challenges:

• Translating Complexity: The primary challenge will be translating highly complex AI models and vast datasets into intuitive, user-friendly experiences that drive actionable insights.

• Navigating Ambiguity: Working with cutting-edge AI technologies often involves inherent ambiguity. Designers must be comfortable defining solutions with incomplete information and adapting as the technology evolves.

• Cross-Functional Alignment: Ensuring buy-in and effective collaboration among diverse stakeholders (product, engineering, data science, leadership) with potentially different priorities and technical understandings.

• Ethical AI Design: Designing AI systems that are fair, transparent, and trustworthy, addressing potential biases and ensuring responsible data usage.

• Keeping Pace with Technology: The rapid evolution of AI and data science requires continuous learning and adaptation to new tools, techniques, and best practices.

Learning & Development Opportunities:

• AI & Data Science Immersion: Deepen understanding of machine learning, AI frameworks, and data science methodologies through internal training, workshops, and collaboration with experts.

• Advanced Design Techniques: Explore specialized areas like agentic workflow design, conversational UI for AI, and advanced data visualization.

• Leadership Development: Opportunities to hone skills in strategic thinking, portfolio management, stakeholder influence, and team mentorship.

• Industry Exposure: Attend relevant conferences, webinars, and industry events focused on AI, data, UX, and the automotive/mobility sectors.

• Formal Education/Certifications: Potential for tuition assistance for further degrees or certifications in relevant fields.

📝 Enhancement Note: The challenges are inherent to working at the frontier of AI and data. The growth opportunities are significant, positioning this role as a key player in Ford's technological transformation.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you designed a product or feature that involved complex data or AI. What was the problem, your approach, and the outcome?" (Focus on process, data translation, and impact.)

• "How do you approach user research for AI-driven products where user intuition might be limited?" (Emphasize qualitative methods, iterative testing, and building trust.)

• "Imagine we want to build an AI assistant for our product managers to help them analyze market trends. What would be your first steps in designing this?" (Showcase design thinking, user needs assessment, and iteration.)

Company & Culture Questions:

• "What excites you about Ford's future in mobility and AI?" (Research Ford's recent announcements, AI initiatives, and vision.)

• "How do you see your design philosophy aligning with Ford's commitment to innovation and customer experience?" (Connect your personal values and approach to Ford's mission.)

• "Describe a situation where you had to influence stakeholders who were resistant to a design recommendation. How did you handle it?" (Demonstrate communication, persuasion, and data-backed arguments.)

Portfolio Presentation Strategy:

• Structure: For each project, clearly outline: Problem, Your Role, Target User, Process (Research, Ideation, Design, Testing), Solution, and Outcome/Impact.

• Visuals: Use high-quality mockups, flows, and data visualizations. Embed short videos or animated prototypes if possible to showcase interactivity.

• Storytelling: Weave a narrative that highlights your problem-solving skills, design rationale, and the value your work delivered. Emphasize the "why" behind your decisions.

• AI/Data Emphasis: For AI projects, explain the AI component, how users interact with it, and how you ensured clarity and trust. For data projects, focus on how you made complex data understandable and actionable.

• Conciseness: Be prepared to present your key projects within a specific timeframe (e.g., 15-20 minutes per project) and answer detailed questions.

📝 Enhancement Note: Preparation should focus on demonstrating a strong understanding of AI/data concepts, a robust design process, excellent communication skills, and the ability to integrate into a large, innovative automotive company.

📌 Application Steps

To apply for this Senior Product Designer position:

• Submit your application through the Ford Motor Company careers portal via the provided link.

• Portfolio Customization: Tailor your resume and cover letter to highlight your experience with AI, data analytics, complex systems design, and user research. Ensure your portfolio is readily accessible and showcases projects most relevant to this role.

• Resume Optimization: Integrate keywords from the job description, such as "AI," "data visualization," "user research," "interaction design," "prototyping," "stakeholder management," and "design thinking." Quantify achievements whenever possible (e.g., "Improved user task completion by X%").

• Interview Preparation: Practice articulating your design process, case studies, and responses to common interview questions, focusing on your experience with AI and data-driven products. Prepare specific examples for behavioral questions.

• Company Research: Deeply understand Ford's current strategy in AI, data analytics, and future mobility. Familiarize yourself with their values and recent innovations to demonstrate genuine interest and alignment.

⚠️ 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.