Design & User Experience Topics
User experience design, frontend architecture, and design systems. Includes UX principles, accessibility, and design documentation.
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.
Rapid Problem Definition
Evaluates the ability to quickly synthesize an ambiguous brief into a clear problem statement, scope, constraints, and measurable success criteria. Assesses timeboxed prioritization, clarifying assumptions, identification of edge cases and risks, formulation of testable hypotheses, and succinct stakeholder alignment under pressure.
Artificial Intelligence Assisted Workflows
Covers how professionals use AI tools to accelerate their day to day work: selecting appropriate use cases for AI assistance, iterating on prompts and instructions to get useful output, generating drafts, variations, or code and evaluating them critically, integrating AI generated output into one's own deliverables without introducing errors, validating outputs against requirements, quality standards, or user needs, and recognizing ethical concerns such as bias, over reliance, and misattributed authorship when applying AI in professional work.