What problem does it solve? Interviewers and candidates lack structured, scenario-based question banks for data science and analytics roles, making it hard to assess statistical rigor, experimentation judgment, and business communication beyond memorized frameworks. ## Core Features & Use Cases - Topic Coverage: Provides question ladders across metric anomaly attribution, A/B test design, hypothesis testing, metric system design, SQL, causal inference, user segmentation, forecasting, business storytelling, event tracking, and campaign ROI evaluation. - Signal-Based Evaluation: Each topic lists danger signals and expected signals so interviewers can distinguish candidates who recite frameworks from those who reason through real scenarios. - Resume-Linked Probing: Supplies follow-up hooks that tie questions to claims on a candidate's resume, such as A/B tests, metric systems, or prediction models. - Use Case: When a candidate's resume mentions building an experimentation platform, load this skill to ask about sample size calculation, SRM detection, guardrail metrics, and how conflicting results were resolved. ## Quick Start Load the data-science skill and generate interview questions for a candidate whose resume mentions A/B testing and metric system design.
