Lead UX Researcher, AI Enablement

📍 Job Overview

Job Title: Lead UX Researcher, AI Enablement

Company: Target

Location: Minneapolis, MN, United States

Job Type: Full-time

Category: User Experience Research (UXR) / Artificial Intelligence (AI) Enablement

Date Posted: August 18, 2026

Experience Level: 7+ years (Mid-Senior to Lead level)

Remote Status: Hybrid

🚀 Role Summary

• Spearhead the research strategy and learning agenda for AI-enabled and agentic experiences within Target's technology and product ecosystem.

• Drive foundational and applied mixed-method research to uncover user needs, adoption barriers, and critical factors for responsible AI use among technical practitioners and enterprise team members.

• Translate complex qualitative and quantitative research findings, combined with behavioral and operational data, into actionable recommendations that influence product strategy, roadmaps, and investment decisions.

• Collaborate extensively with cross-functional teams including UX, Product Management, Technology, Data Science, Security, and business stakeholders to shape the future of AI within Target.

📝 Enhancement Note: This role is positioned as a Lead UX Researcher, indicating significant autonomy in defining research direction, influencing strategy, and mentoring junior researchers. The focus on "AI Enablement" suggests a strategic initiative within Target to leverage AI effectively for both internal operations (team members, technical practitioners) and potentially outward-facing products, with a strong emphasis on responsible adoption and human oversight. This is distinct from a typical product UX role and leans heavily into understanding complex systems, user workflows, and the impact of emerging technologies.

📈 Primary Responsibilities

• Establish and lead the overarching research strategy and learning agenda for AI Enablement initiatives, directly influencing portfolio priorities and strategic investment decisions.

• Design and execute foundational and applied research studies using a mixed-methodology approach across various products, platforms, pilots, and live AI-enabled experiences.

• Define target audiences, map user journeys, and identify adoption barriers through robust frameworks such as personas, journey mapping, and service blueprints.

• Rigorously evaluate AI-enabled concepts, pilot programs, and live experiences against key criteria including usefulness, user confidence, transparency, human oversight, accessibility, and responsible adoption.

• Synthesize qualitative research insights with behavioral data, analytics, operational signals, system performance metrics, and external benchmarks to pinpoint key opportunities and measure the impact of AI initiatives.

• Translate research evidence into clear, compelling, and actionable recommendations for product strategy, development roadmaps, platform enhancements, tooling, enablement programs, governance frameworks, and resource allocation.

• Foster strong partnerships across UX, Product, Technology, Data Science, Privacy, Legal, and various business units to shape high-value research questions and guide the development of innovative AI experiences.

• Activate research insights through impactful storytelling, mentor and guide other UX researchers, and champion the advancement of rigorous, inclusive, ethical, and responsible research practices within the AI domain.

📝 Enhancement Note: The responsibilities highlight a strategic leadership component, where the researcher is expected to not only execute research but also define its scope and influence business outcomes. The emphasis on combining qualitative insights with quantitative data (behavioral, operational) is critical for this role, requiring a blend of deep user understanding and analytical rigor. The mention of "agentic experiences" and "human oversight" points towards a focus on AI systems that operate with a degree of autonomy, necessitating research into trust, control, and ethical considerations.

🎓 Skills & Qualifications

Education: Bachelor's or Master's degree in Human-Computer Interaction (HCI), Psychology, Cognitive Science, Anthropology, Sociology, Computer Science, or a related field. A Ph.D. in a relevant discipline is often a strong asset for lead-level research roles.

Experience: Minimum of 7 years of progressive experience in User Experience (UX) Research, Product Research, Applied Research, Human-Computer Interaction (HCI), Behavioral Science, or a closely related field.

Required Skills:

• Deep Expertise in UX Research Methodologies: Proven mastery of both qualitative (e.g., interviews, usability testing, ethnography) and quantitative (e.g., surveys, A/B testing, data analysis, logging analysis) research methods.

• AI/Complex Technology Research: Demonstrated experience researching AI-enabled products, developer tools, complex software platforms, or similar sophisticated technologies, with a focus on understanding adoption challenges and shifts in user workflows.

• Strategic Research Planning: Proven ability to define and lead research strategy across multiple complex products, platforms, services, and concurrent workstreams, aligning research objectives with business goals.

• Data Synthesis & Insight Generation: Strong capability to integrate qualitative research findings with behavioral data, analytics, operational metrics, and system performance data to identify actionable opportunities and quantify impact.

• Framework Development: Experience creating and utilizing durable research frameworks such as personas, journey mapping, and service blueprints to define audiences, understand user needs, and map out complex user flows.

• Cross-Functional Collaboration: Fluency in partnering effectively with Engineering, Architecture, Data Science, Security, Product Management, Privacy, and other technical and business teams.

• Systems Thinking: Ability to understand and analyze complex systems, interdependencies, and their impact on user experience and adoption.

• Communication & Influence: Excellent storytelling, facilitation, and presentation skills, with a proven ability to navigate ambiguity, lead multiple initiatives, and influence senior leaders and stakeholders.

• Mentorship & Practice Advancement: Experience mentoring junior researchers and actively contributing to the improvement and ethical application of research practices, with a focus on inclusivity, accessibility, privacy, and responsibility.

Preferred Skills:

• Experience researching AI governance, ethics, transparency, and human-AI collaboration.

• Familiarity with research operations (ResearchOps) principles and best practices.

• Experience with enterprise software development lifecycles and tools.

• Knowledge of machine learning concepts and their user implications.

• Experience in retail technology or large-scale consumer-facing digital platforms.

📝 Enhancement Note: The 7+ years of experience requirement, coupled with the "Lead" title, signifies an expectation of strategic leadership, independent problem-solving, and the ability to mentor others. The emphasis on researching AI and complex technical systems, alongside data synthesis and cross-functional influence, points to a role that requires both deep research craft and strong business acumen. The hybrid work arrangement suggests that candidates should be comfortable with a mix of remote and in-office collaboration.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrated Research Strategy: Showcase examples of how you've defined and executed research strategies for complex products or initiatives, aligning research questions with business objectives and influencing strategic decisions.

• Mixed-Methodology Case Studies: Present detailed case studies that highlight your proficiency in applying both qualitative and quantitative research methods to solve significant user problems, particularly within technical or AI-related contexts.

• Data Synthesis & Impact: Include examples where you've effectively combined diverse data sources (qualitative insights, behavioral data, operational metrics) to drive product improvements or strategic shifts, clearly articulating the measurable outcomes.

• Durable Framework Application: Provide evidence of creating or utilizing frameworks like personas, journey maps, or service blueprints to communicate deep user understanding and inform product development and strategic planning.

• Cross-Functional Collaboration & Influence: Illustrate instances where your research has directly influenced decisions made by Product, Engineering, Data Science, or senior leadership, demonstrating your ability to build consensus and drive adoption of insights.

Process Documentation:

• Research Design & Execution: Document your process for planning, designing, and executing end-to-end research projects, from defining objectives to selecting methodologies and recruiting participants, especially for AI-related topics.

• Insight Activation & Storytelling: Outline your approach to synthesizing research findings into compelling narratives and actionable recommendations, and how you deliver these to various stakeholders to ensure impact and adoption.

• Ethical & Responsible Research Practices: Detail your commitment to and implementation of ethical, inclusive, accessible, privacy-conscious, and responsible research practices, particularly relevant for AI enablement.

• Collaboration & Mentorship: Describe your methods for collaborating with cross-functional teams and how you mentor and guide other researchers to enhance team capabilities and research quality.

📝 Enhancement Note: For a Lead UX Researcher role, especially one focused on AI Enablement, the portfolio should emphasize strategic thinking, impact on business outcomes, and the ability to handle complex, ambiguous problems. Case studies should clearly articulate the "why" behind the research, the methodologies chosen, the insights derived, and the tangible results or influence achieved. Given the AI focus, demonstrating an understanding of ethical considerations, data privacy, and responsible technology adoption within research is crucial.

💵 Compensation & Benefits

Salary Range: $132,000 - $238,000 per year.

Benefits:

• Comprehensive Health Coverage: Eligible team members and their dependents receive comprehensive health benefits including medical, vision, and dental insurance.

• Life Insurance: Life insurance coverage is provided.

• Retirement Savings: 401(k) plan is available for eligible team members.

• Employee Discount: A valuable employee discount on Target purchases.

• Disability Insurance: Both short-term and long-term disability coverage are provided.

• Paid Time Off: Generous paid time off includes sick leave, paid national holidays, and paid vacation days.

• Additional Programs: Access to a wide array of additional benefits covering financial, educational, and well-being aspects, detailed at https://corporate.target.com/careers/benefits.

Working Hours: The role is full-time, typically expected to align with a standard 40-hour work week. While the role is hybrid, specific daily onsite requirements will vary based on project needs and team collaboration.

📝 Enhancement Note: The provided salary range is competitive for a Lead UX Researcher role in a major metropolitan area like Minneapolis, especially for a company of Target's scale and a specialized area like AI Enablement. The benefits listed are comprehensive and standard for large corporations, offering good support for employee well-being and financial security. The "Hybrid/Flex for Your Day" arrangement implies a degree of flexibility but also requires significant in-office presence for collaboration.

🎯 Team & Company Context

🏢 Company Culture

Industry: Retail (with a significant focus on technology and digital innovation). Target operates at the intersection of retail and technology, leveraging its extensive physical and digital presence to create seamless guest experiences. The company is increasingly investing in data, AI, and advanced analytics to drive business outcomes and enhance operations.

Company Size: Target is a large, Fortune 500 corporation. The company employs hundreds of thousands of team members globally, indicating a vast and complex organizational structure with numerous internal departments and teams. This scale offers opportunities for broad impact and exposure to diverse business challenges.

Founded: Target was founded in 1902, giving it a long history of innovation and adaptation in the retail landscape. This longevity suggests a stable and established organization with deep expertise in its core business, now actively expanding its technological capabilities.

Team Structure:

• Global UX Team: The UX team at Target is a significant, multidisciplinary group comprising design, content, research, and accessibility practitioners. This team is dedicated to enhancing digital experiences for guests, team members, and partners.

• AI Enablement Focus: This specific role sits within the AI Enablement research function, suggesting a dedicated group focused on the strategic integration and adoption of AI technologies across the enterprise.

• Cross-Functional Collaboration: The role emphasizes close collaboration with UX designers, product managers, data scientists, engineers, architects, security teams, and various business units. This indicates a highly matrixed and collaborative environment where research insights are integrated into product development and strategic planning.

Methodology:

• Data-Driven Decision Making: Target relies heavily on data analysis, behavioral insights, and operational metrics to inform strategic and product decisions. This role is central to this approach by providing crucial user understanding.

• Human-Centered Design & Research: While data-intensive, the company's UX team prioritizes a human-centered approach, ensuring that technology solutions are designed with the needs and experiences of users (guests and team members) at their core.

• Agile & Iterative Development: Expect a dynamic environment that embraces agile methodologies, requiring researchers to adapt to evolving priorities and provide timely insights to support iterative development cycles, especially in fast-moving AI areas.

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

📝 Enhancement Note: Target's position as a major retailer with a strong technology arm means this role operates within a context of immense scale, data availability, and direct customer impact. The "AI Enablement" aspect suggests the company is strategically investing in AI to optimize operations, personalize experiences, and potentially develop new capabilities. The culture likely balances a strong corporate structure with a drive for innovation, particularly in digital and AI domains.

📈 Career & Growth Analysis

Operations Career Level: This role is classified as a "Lead" UX Researcher. This signifies a senior individual contributor position with significant responsibility for setting research direction, influencing strategy, and mentoring other researchers. It's a step beyond a senior researcher, implying leadership in a specific domain (AI Enablement) and a broader impact on how research is conducted and utilized within the organization.

Reporting Structure: While not explicitly stated, Lead UX Researchers typically report to a Director or Senior Manager of UX Research or a related Head of Product/Technology function. The role will involve close partnership with Product Managers, Engineering Leads, and Data Science leads for the AI Enablement domain.

Operations Impact: The "AI Enablement" focus means this role has a direct impact on how Target leverages AI across its business. This includes optimizing internal workflows for team members and technical practitioners, improving the efficiency and effectiveness of AI development and deployment, and ensuring responsible and ethical AI adoption. The insights generated will shape the future of Target's technological capabilities and operational efficiency.

Growth Opportunities:

• Domain Specialization: Deepen expertise in AI research, responsible AI, agentic systems, and human-AI interaction, becoming a recognized subject matter expert within Target.

• Leadership Development: Progress into management roles (e.g., UX Research Manager) overseeing a team of researchers, or take on larger, more complex strategic initiatives within the UX organization.

• Cross-Functional Leadership: Expand influence across different business units or product areas, potentially leading research efforts for broader AI initiatives or new technology frontiers.

• Skill Expansion: Develop skills in areas like generative AI, machine learning operations (MLOps), and advanced data analytics to complement UX research expertise.

📝 Enhancement Note: The Lead UX Researcher title implies a clear path for career advancement within Target's UX or technology organizations. The focus on AI enablement positions the candidate at the forefront of a critical strategic initiative, offering significant visibility and opportunity to shape future technology adoption. Growth paths likely involve deeper specialization, broader strategic influence, or team leadership.

🌐 Work Environment

Office Type: The role is based at Target HQ in Minneapolis, MN, and is designated as "Hybrid/Flex for Your Day." This indicates a modern corporate office environment designed to support collaborative work, with various meeting spaces, team areas, and potentially quiet zones.

Office Location(s): The primary location is 1000 Nicollet Mall, Minneapolis, MN 55403-2542. This is a central downtown Minneapolis location, likely offering good access to public transportation and amenities.

Workspace Context:

• Collaborative Hub: The office serves as a hub for in-person collaboration, team meetings, brainstorming sessions, and strategic discussions, crucial for a role involving close partnerships with diverse teams.

• Technology Enabled: Expect a well-equipped workspace with necessary technology for research, communication, and data analysis, supporting both in-office and remote aspects of the hybrid model.

• Team Interaction: The hybrid model necessitates intentional engagement with team members both virtually and during designated in-office days, fostering a strong sense of team cohesion and shared purpose.

Work Schedule: The work schedule is full-time, typically around 40 hours per week. The hybrid arrangement allows for flexibility in how days are structured between remote and in-office work, based on team needs and individual task requirements. This flexibility can be beneficial for deep work requiring focus, while in-office days facilitate critical collaboration and strategic alignment.

📝 Enhancement Note: The "Hybrid/Flex for Your Day" model suggests a dynamic work environment where team members are expected to be present in the office for specific collaborative needs, while also leveraging remote work for focused tasks. Candidates should be comfortable with this level of autonomy and responsibility for managing their presence and productivity across different work settings.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will likely conduct an initial phone screen to assess basic qualifications, experience, and cultural fit.

• Hiring Manager Interview: A conversation with the hiring manager to delve deeper into your experience, research philosophy, and understanding of AI enablement challenges.

• Portfolio Presentation & Deep Dive: A critical stage where you will present a selection of your work, focusing on case studies that demonstrate your strategic approach, mixed-method expertise, data synthesis capabilities, and impact on product/business. Be prepared to discuss your process, decision-making, and how you navigated complex research problems.

• Cross-Functional Interviews: Interviews with key collaborators such as Product Managers, UX Designers, Data Scientists, and Engineering leads to assess your ability to partner effectively and influence across disciplines.

• Research Team Interview: Interaction with other UX researchers to evaluate your research craft, mentorship potential, and alignment with the team's values and practices.

• Final Round/Executive Interview: Potentially a final conversation with senior leadership to discuss strategic vision and overall fit for the Lead role.

Portfolio Review Tips:

• Curate Strategically: Select 2-3 comprehensive case studies that best represent your experience in strategic research planning, mixed-method execution, data synthesis, and influencing outcomes, particularly in complex or AI-related domains.

• Focus on Impact: For each case study, clearly articulate the problem, your approach, the insights discovered, the recommendations made, and the tangible business or user impact achieved. Quantify results whenever possible.

• Highlight AI/Technical Expertise: If possible, include a case study that showcases your experience researching AI-enabled products, developer tools, or other complex technical systems. Emphasize your understanding of user adoption, trust, and responsible use.

• Demonstrate Process: Be ready to walk through your research process in detail, explaining your rationale for methodology choices, how you handled challenges, and how you synthesized findings.

• Tailor to Target: Research Target's products, values, and current AI initiatives to tailor your presentation and demonstrate how your skills align with their specific needs.

Challenge Preparation:

• Anticipate Strategy Questions: Prepare to discuss how you would approach setting a research strategy for a new AI enablement initiative at Target, considering ambiguity and evolving priorities.

• Behavioral Questions: Be ready to answer questions about how you've handled difficult stakeholders, navigated ambiguity, mentored junior researchers, or advocated for user needs in challenging situations.

• Problem-Solving Scenarios: You might be presented with a hypothetical research problem related to AI adoption or user experience and asked to outline your approach.

• Ethical Considerations: Prepare to discuss your perspective on ethical research practices, data privacy, and responsible AI development.

📝 Enhancement Note: The interview process is likely robust, given the Lead level and specialized domain. A strong portfolio showcasing strategic impact, mixed-method rigor, and cross-functional influence is paramount. Candidates should prepare to articulate their thought process and demonstrate how their research directly contributes to business objectives, especially in the context of AI adoption and enablement.

🛠 Tools & Technology Stack

Primary Tools:

• Research Platforms: Proficiency with various qualitative research tools (e.g., UserTesting, Lookback, Maze, Dovetail, Qualtrics) for usability testing, interviews, and diary studies.

• Survey & Data Collection: Experience with survey platforms (e.g., SurveyMonkey, Google Forms, Qualtrics) for quantitative data gathering.

• Data Analysis Software: Familiarity with statistical software (e.g., SPSS, R, Python libraries like Pandas/NumPy) or qualitative data analysis software (e.g., NVivo, ATLAS.ti) for deep analysis.

• Collaboration Tools: Proficiency with tools like Jira, Confluence, Miro, Mural, and Microsoft Teams/Slack for project management, ideation, and team communication.

Analytics & Reporting:

• Web/App Analytics: Experience with tools like Google Analytics, Adobe Analytics, or internal tracking systems to analyze user behavior data.

• BI & Visualization Tools: Familiarity with platforms like Tableau, Power BI, or Looker for creating dashboards and communicating data insights.

• Data Warehousing/Databases: Understanding of how to access and query data from data warehouses (e.g., Snowflake, BigQuery) or databases.

CRM & Automation:

• While not a core CRM role, understanding how user data is managed within systems like Salesforce or internal CRMs can be beneficial for context.

• Familiarity with automation concepts relevant to research workflows (e.g., participant recruitment, data processing) is a plus.

📝 Enhancement Note: For a Lead UX Researcher, especially in AI Enablement, the emphasis will be on advanced data analysis and synthesis tools rather than core CRM or automation platforms. The ability to leverage analytics and BI tools to complement qualitative findings is critical. Proficiency in tools that support mixed-methods research and efficient data analysis will be highly valued.

👥 Team Culture & Values

Operations Values:

• Guest Obsession: A foundational value at Target, meaning all efforts, including AI enablement research, should ultimately serve to improve the experience for Target's guests.

• Data-Driven: A strong emphasis on using data and analytics to inform decisions, measure impact, and drive continuous improvement in both products and processes.

• Inclusivity & Belonging: Commitment to creating equitable experiences for all guests and team members, which extends to ensuring research practices are inclusive and AI solutions are accessible and fair.

• Innovation & Agility: A culture that encourages experimentation, embraces new technologies like AI, and adapts quickly to changing market dynamics and user needs.

• Collaboration: A belief in the power of teamwork and cross-functional partnerships to achieve ambitious goals, essential for complex initiatives like AI enablement.

Collaboration Style:

• Partnership-Oriented: Researchers are expected to be proactive partners with Product, Engineering, and Data Science teams, working collaboratively to define problems and co-create solutions.

• Insight-Driven: The team values research that provides clear, actionable insights that directly inform strategy and development, rather than just reporting findings.

• Mentorship & Knowledge Sharing: A culture where senior members mentor junior staff, and knowledge is shared openly across the UX organization to elevate collective expertise.

• Continuous Improvement: An environment that encourages feedback, learning from both successes and failures, and constantly refining research processes and methodologies.

📝 Enhancement Note: Target's strong corporate values, particularly Guest Obsession and Data-Driven decision-making, will heavily influence how research is conducted and applied within AI Enablement. The culture likely supports innovation but within a structured, data-informed framework, emphasizing collaboration and measurable impact.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Ambiguity in AI: The AI landscape is rapidly evolving. Researchers will face ambiguity regarding future AI capabilities, user adoption patterns, and ethical considerations. Developing frameworks and research approaches to address this uncertainty will be key.

• Balancing Technical Complexity with User Needs: Effectively translating highly technical AI concepts into user-centric problems and solutions requires bridging significant gaps between engineering and end-user understanding.

• Measuring Impact of AI Enablement: Quantifying the direct impact of AI enablement research on business outcomes, especially for internal tools and workflows, can be challenging. Developing robust metrics and reporting mechanisms will be crucial.

• Ensuring Responsible AI Adoption: Researching and promoting the ethical, transparent, and secure use of AI, mitigating bias, and ensuring human oversight in complex agentic systems presents a significant ethical and practical challenge.

Learning & Development Opportunities:

• AI & ML Specialization: Opportunities to deepen knowledge of AI/ML concepts, generative AI, and agentic systems through internal training, external courses, and hands-on research.

• Strategic Influence: Develop skills in influencing product roadmaps, investment decisions, and enterprise-wide strategy through impactful research leadership.

• Mentorship & Team Leadership: Gain experience mentoring junior researchers, leading research initiatives, and potentially stepping into management roles.

• Cross-Disciplinary Learning: Collaborate closely with Data Science, Engineering, and Product teams, gaining exposure to their workflows and technical challenges.

📝 Enhancement Note: The AI Enablement domain is inherently challenging due to its nascent nature and rapid evolution. Success in this role will require adaptability, a strong analytical mindset, and a commitment to ethical innovation. The growth opportunities are significant, positioning the researcher to be a leader in a critical area of future business strategy.

💡 Interview Preparation

Strategy Questions:

• "How would you approach establishing a research strategy for 'AI Enablement' at Target, considering the broad scope of AI applications and the need to prioritize?" - Preparation: Focus on frameworks for defining learning agendas, identifying key stakeholder needs, and aligning research with business objectives.

• "Describe a time you had to research a complex, technical product or system. What were the challenges, and how did you ensure user needs were met?" - Preparation: Prepare a case study that highlights your ability to understand technical concepts and translate them into user-centric insights, emphasizing mixed-methodologies and data synthesis.

• "How do you ensure your research findings lead to actionable recommendations and influence product strategy, especially when dealing with senior leadership or technical teams?" - Preparation: Detail your methods for insight activation, storytelling, and stakeholder management, providing examples of successful influence. Company & Culture Questions:

• "What interests you about Target's approach to AI and technology, and how does your research philosophy align with Target's values like Guest Obsession and Data-Driven decision-making?" - Preparation: Research Target's recent tech initiatives, AI strategy, and company values. Frame your answers to demonstrate cultural fit and strategic alignment.

• "How would you foster collaboration between UX research, Data Science, and Engineering teams in the context of AI development?" - Preparation: Discuss your experience building cross-functional relationships, understanding different team perspectives, and facilitating shared understanding.

• "What are your thoughts on ethical considerations in AI research and development, and how do you incorporate them into your practice?" - Preparation: Be ready to discuss principles of responsible AI, data privacy, bias mitigation, and transparency, providing examples of how you've addressed these. Portfolio Presentation Strategy:

• Structure for Impact: Organize your portfolio presentation logically, typically starting with an overview of your experience and then diving into 2-3 detailed case studies. For each case study, clearly define the problem, your role and approach, the methodologies used, key insights, recommendations, and the resulting impact.

• Quantify Outcomes: Wherever possible, use metrics and data to demonstrate the impact of your research. This is especially critical for a role focused on enablement and efficiency.

• Showcase Strategic Thinking: Emphasize how your research informed strategic decisions, influenced roadmaps, or led to significant product improvements, rather than just reporting usability issues.

• Articulate Your Process: Be prepared to explain the "why" behind your methodological choices and how you navigated challenges or trade-offs.

• Engage Your Audience: Make your presentation interactive. Use visuals effectively and be ready to answer detailed questions about your work and your thought process.

📝 Enhancement Note: Interview preparation for a Lead role should focus on demonstrating strategic thinking, leadership potential, and a deep understanding of complex research challenges, particularly those related to AI. Candidates should be ready to articulate their decision-making process, their ability to influence, and their commitment to ethical and impactful research.

📌 Application Steps

To apply for this Lead UX Researcher, AI Enablement position:

• Submit your application through the provided Workday link on Target's careers portal.

• Tailor Your Resume: Customize your resume to highlight your 7+ years of experience in UX research, specifically emphasizing experience with AI-enabled products, complex systems, mixed-methodologies, data synthesis, and cross-functional collaboration. Use keywords from the job description.

• Curate Your Portfolio: Select 2-3 of your most impactful research projects that best showcase your strategic leadership, research craft, ability to influence outcomes, and experience with technical or AI-related domains. Ensure your portfolio clearly articulates problem, process, insights, and impact.

• Prepare Your Presentation: Practice presenting your portfolio case studies concisely and engagingly, focusing on quantifiable results and strategic influence. Be ready to discuss your research process and decision-making in detail.

• Research Target: Deeply understand Target's business, its technology initiatives, its UX philosophy, and its company values. Prepare to articulate how your skills and experience align with their specific needs and culture, especially concerning AI enablement.

⚠️ 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.