##### Question DoorDash wants to understand and forecast the difference between on-platform menu prices and the same items' in-store prices (the "inflation gap"). Design an end-to-end analysis that: 1. **Measures the current gap.** Collect and clean matched item-level prices, then construct a robust index that summarizes how much higher (or lower) platform prices are versus in-store prices, both as a level (markup) and as an inflation differential (the change in the gap over time). 2. **Matches identical items.** Describe how you would link a platform item at a given store and time to the same item in-store (normalization, blocking, similarity scoring, one-to-one resolution, and how you would validate match quality). 3. **Quantifies uncertainty.** Produce confidence intervals for the gap when no A/B test is possible, accounting for items clustering within stores and prices persisting over time. 4. **Forecasts the gap.** Build a time-series model (with exogenous drivers such as CPI, fuel, wages, and policy changes) to forecast the gap, and produce prediction intervals. 5. **Sizes the study / computes MDE.** Without randomization, derive the sample size and minimum detectable effect for an observational design (e.g., pre/post or difference-in-differences), incorporating clustering and autocorrelation design effects. Cover data sources, matching identical items, the choice of price index, bootstrapped vs. model-based confidence intervals, the forecasting model class, and the power-analysis assumptions.

This DoorDash data scientist onsite question evaluates statistical analysis, item-level matching pipelines, price-index construction, time-series forecasting, and uncertainty quantification for measuring the platform-versus-in-store price inflation gap. Candidates must build matched-item log price gaps, quantify uncertainty with cluster- and time-aware methods, forecast with SARIMAX/state-space models, and compute confidence intervals or sample-size/MDE requirements for an observational (pre-post or difference-in-differences) design with no A/B test.

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This is a medium difficulty Statistics & Math question, commonly asked during Onsite rounds at DoorDash.

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This question is commonly asked for Data Scientist candidates at DoorDash during technical interviews.

Forecast and Analyze DoorDash Menu Price Inflation Gap

Question

DoorDash wants to understand and forecast the difference between on-platform menu prices and the same items' in-store prices (the "inflation gap"). Design an end-to-end analysis that:

Measures the current gap. Collect and clean matched item-level prices, then construct a robust index that summarizes how much higher (or lower) platform prices are versus in-store prices, both as a level (markup) and as an inflation differential (the change in the gap over time).
Matches identical items. Describe how you would link a platform item at a given store and time to the same item in-store (normalization, blocking, similarity scoring, one-to-one resolution, and how you would validate match quality).
Quantifies uncertainty. Produce confidence intervals for the gap when no A/B test is possible, accounting for items clustering within stores and prices persisting over time.
Forecasts the gap. Build a time-series model (with exogenous drivers such as CPI, fuel, wages, and policy changes) to forecast the gap, and produce prediction intervals.
Sizes the study / computes MDE. Without randomization, derive the sample size and minimum detectable effect for an observational design (e.g., pre/post or difference-in-differences), incorporating clustering and autocorrelation design effects.

Cover data sources, matching identical items, the choice of price index, bootstrapped vs. model-based confidence intervals, the forecasting model class, and the power-analysis assumptions.

Question

DoorDash wants to understand and forecast the difference between on-platform menu prices and the same items' in-store prices (the "inflation gap"). Design an end-to-end analysis that:

Measures the current gap. Collect and clean matched item-level prices, then construct a robust index that summarizes how much higher (or lower) platform prices are versus in-store prices, both as a level (markup) and as an inflation differential (the change in the gap over time).
Matches identical items. Describe how you would link a platform item at a given store and time to the same item in-store (normalization, blocking, similarity scoring, one-to-one resolution, and how you would validate match quality).
Quantifies uncertainty. Produce confidence intervals for the gap when no A/B test is possible, accounting for items clustering within stores and prices persisting over time.
Forecasts the gap. Build a time-series model (with exogenous drivers such as CPI, fuel, wages, and policy changes) to forecast the gap, and produce prediction intervals.
Sizes the study / computes MDE. Without randomization, derive the sample size and minimum detectable effect for an observational design (e.g., pre/post or difference-in-differences), incorporating clustering and autocorrelation design effects.

Cover data sources, matching identical items, the choice of price index, bootstrapped vs. model-based confidence intervals, the forecasting model class, and the power-analysis assumptions.

Forecast and Analyze DoorDash Menu Price Inflation Gap

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Forecast and Analyze DoorDash Menu Price Inflation Gap

Quick Overview

Question

Solution

Submit Your Answer to Earn 20XP