End to end planning and design of research studies to rigorously answer product, user experience, or scientific questions. Candidates should be able to translate business or product problems into clear and testable research questions and hypotheses and convert those questions into feasible and valid study plans. Core skills include selecting appropriate qualitative, quantitative, or mixed methods, defining primary outcomes and success metrics, aligning sampling strategy and inclusion and exclusion criteria, estimating sample sizes and articulating precision and power considerations, designing recruitment approaches and consent procedures, drafting interview guides survey items and measurement instruments with attention to reliability and validity, planning data collection workflows and quality controls, and outlining statistical and qualitative analysis plans and integration strategies for mixed methods. Candidates should also be able to identify potential confounds and threats to internal and external validity and propose mitigation approaches, scope studies to remain feasible under time and resource constraints, plan logistics timelines and resource allocation, pilot and iterate instruments, address ethical and regulatory requirements such as institutional review board review and data privacy, and communicate research plans limitations and actionable findings to stakeholders. Interviewers may probe trade offs among methodologies bias mitigation strategies reproducibility and documentation practices how the candidate managed scope and stakeholder expectations and how preliminary findings or stakeholder input influenced the evolution of research questions and study scope while avoiding scope creep.
HardTechnical
0 practiced
In a randomized experiment executed across multiple countries you observe heterogeneous treatment effects by site. Propose an analysis plan using hierarchical/multilevel models to estimate pooled and site-specific effects, explain shrinkage and partial pooling trade-offs, when to prefer random-effects vs site-fixed effects, and pre-specified rules for reporting and product decisions (e.g., pool vs local rollout).
MediumTechnical
0 practiced
You developed a 10-item Likert scale intended to measure 'user confidence' in completing a task. Outline a validation plan: steps to assess internal consistency (e.g., Cronbach's alpha), construct validity (convergent and discriminant), exploratory and confirmatory factor analysis (EFA/CFA) workflow, test-retest reliability, and sample-size guidelines for factor analysis. Describe actions if EFA reveals cross-loading items.
HardTechnical
0 practiced
You're hired to build a five-person research team supporting both rapid product analytics and publication-quality academic research. Propose a hiring plan with role descriptions and skill mix (e.g., applied data scientist, causal inference specialist, survey/qualitative researcher, ML engineer, research program manager), mentorship and career-development structures, peer-review and pre-registration processes, and team KPIs that balance delivery and academic output.
HardTechnical
0 practiced
Design an observational study to estimate the long-term causal effect of a redesigned onboarding flow on 12-month retention across three regions with staggered rollout and partial instrumentation. Propose two quasi-experimental strategies (e.g., difference-in-differences with staggered adoption, synthetic control, or instrumental variables using rollout schedule), state identification assumptions for each, list pre-analysis diagnostics (e.g., pre-trend tests, placebo checks), robustness checks, and how you'd communicate residual uncertainty to executives.
HardTechnical
0 practiced
You lead a research group and must decide whether to submit a product-research study with null primary findings to a top-tier conference, archive as a preprint, or keep it as an internal report. Draft criteria to decide venue, discuss preprint timing, data and code sharing considerations including IP/confidentiality, authorship order decisions (industry collaborations), and how to handle peer review when data cannot be fully open.
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