{"blocks": [{"key": "cb88de93", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "0c324d3b", "text": "Customers complain that delivered food often arrives cold. As the data scientist for the delivery quality (Dasher) team, you must diagnose the problem and design a solution.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "8df10482", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "d19ba6ba", "text": "How would you investigate the root causes of cold food deliveries? Which metrics would you track, and what data would you pull? Design an experiment to test a mitigation (e.g., insulated bags, optimized routing). Detail hypothesis, treatment, control, unit of randomization, success metrics, and runtime calculation.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "18e54aa2", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "7c0766e5", "text": "Frame with funnel analysis (prep, pickup, travel time), define quantitative temperature proxy, consider staged A/B test across zones, monitor delivery time, reorder-rate, and complaint-rate.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}