Design & User Experience Topics
User experience design, frontend architecture, and design systems. Includes UX principles, accessibility, and design documentation.
Information Architecture and Content Design
Organizing product content and user interfaces for clarity and discoverability. Topics include information hierarchies, navigation and routing, user flows and journey mapping, wireframing and low fidelity exploration, content organization and labeling, progressive disclosure, dashboard layout and KPI placement, filters and drill downs, and ideation and sketching techniques. Evaluates the ability to align structure with user mental models and to iterate designs based on evidence.
Findings Presentation and Impact
Ability to clearly present analytical findings and insights to stakeholders, and explain how those findings shaped a decision, process, or outcome. Covers structuring a findings narrative (context, evidence, recommendation), choosing the right visualization or format for the data, tailoring depth and language for technical versus non-technical audiences, and demonstrating measurable impact and follow-through on recommendations.
Technical Depth & Areas of Specialization
Every strong candidate has one or more areas of technical depth that go beyond generalist knowledge. Discuss the area(s) where you have the most depth: how you identify it (a subsystem, technology, domain, or class of problem you gravitate toward), a concrete project or accomplishment that demonstrates that depth, how you actively keep that expertise current (reading, communities, side projects, postmortems), and how that depth changes the way you make trade-offs or collaborate with generalists on your team. Areas of specialization are highly individual and role-dependent (examples span distributed systems reliability, accessibility and design systems, security architecture, data pipelines, performance optimization, mobile platforms) - the interviewer should probe the candidate's own stated specialization rather than assume a fixed domain.
Research Artifacts and Documentation
Skills for creating and managing research artifacts that communicate findings and support decision making, across any research-driven role (UX/design research, data science, market research, academic or applied research). Covers common artifact types: formal research reports, executive summaries, slide presentations, research briefs, personas and journey maps, analysis memos and write-ups, dashboards, and data visualizations. Emphasis on selecting the right artifact for the audience and purpose, balancing comprehensiveness with usability, ensuring clarity and reproducibility of findings, maintaining artifact quality and currency over time, applying templates and version control, and collaborating with stakeholders to disseminate insights effectively.
Progressive Disclosure and Audience Design
Covers the principles and practices of structuring information for multiple audiences by using progressive disclosure to manage cognitive load. Candidates should be able to explain the theory of progressive disclosure, why limiting initial information reduces cognitive load, and when to reveal additional details on demand. This includes concrete strategies for serving beginners and advanced users or different roles and use cases within the same product documentation or interface: layered content (overview then details), summaries with expandable details, quick start guides, step by step tutorials, reference sections, role specific landing pages, contextual help, tooltips, and example driven content. Discuss design tradeoffs such as discoverability versus simplicity, maintaining consistency, versioning and referenceability, and accessibility considerations. Describe how to identify audience needs through personas, user research, and analytics, and how to structure navigation and information architecture so users can find the level of detail they need. Be prepared to give examples of implementation patterns, explain when progressive disclosure is inappropriate, and describe metrics to evaluate success such as task completion, time on task, support volume, heatmaps, and user feedback.
Research Problem Solving
Evaluate how the candidate identifies, frames, and iteratively resolves research or investigation challenges. Expect examples of encountering recruitment difficulties, unexpected findings, stakeholder disagreements, resource or technical constraints, and how the candidate adapted methods in response. Key skills include iterative hypothesis refinement, questioning and testing assumptions, balancing methodological rigor with flexibility, documenting decision making, synthesizing findings, and communicating trade offs to stakeholders. Emphasis is on demonstrating learning from preliminary results and showing a structured approach to refining research questions, methods, and analyses.
Audience Analysis and Information Hierarchy
Assessing stakeholder needs, information priorities, and decision-making requirements. Designing tailored views for different audiences (executives, product managers, analysts). Understanding different decision contexts and how they shape information needs.
Research Philosophy and Alignment
Explain how you approach research: your philosophy on exploratory versus evaluative work, how you choose between quantitative and qualitative methods, and how you synthesize and prioritize insights into actionable findings. Discuss how you integrate research into team decision making, negotiate trade offs and timelines, and align your research standards and values with the stakeholders you work with.
Balancing Research Speed and Rigor
Concerns choosing appropriate research approaches based on decision urgency and confidence needs. Candidates should explain methods for rapid lightweight research to inform fast decisions, and more rigorous studies to build confidence for major investments. Topics include sampling and bias trade offs, triangulating data sources, communicating uncertainty and confidence levels to stakeholders, scaling research velocity through templates and reusable assets, and deciding when a fast answer is sufficient versus when deeper evidence is required.