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This is where I spent most of my time and probably overcomplicated it.
For each metric, identify its distributional nature (binary, continuous, ratio) and select the appropriate hypothesis test (e.g., z-test for proportions, t-test for means, delta method for ratios). Then discuss how to validate assumptions (e.g., normality, independence, variance homogeneity) using diagnostic plots and tests, and consider alternatives like bootstrap or non-parametric tests if assumptions fail.
Pro tip: At Instacart, metrics like take rate and delivery cost per order are ratios of random variables, so the delta method or bootstrap is often needed; mentioning this shows depth beyond textbook tests.
Determine the statistical nature of each metric: conversion rate is a proportion, AOV is a continuous mean, take rate is a ratio of two continuous variables, and delivery cost per order is a continuous mean (or ratio if aggregated).
For conversion rate, use a two-proportion z-test; for AOV and delivery cost per order, use Welch's t-test (unequal variances); for take rate, use the delta method or bootstrap due to its ratio nature.
For z-test: independent Bernoulli trials, large sample size for normal approximation. For t-test: independent samples, approximately normal sampling distribution (CLT), and for Welch's t-test, no equal variance assumption. For delta method: smooth function of asymptotically normal estimators.
Check sample sizes and expected counts for z-test; use QQ plots, histograms, and Shapiro-Wilk for normality; Levene's test for variance homogeneity; and consider bootstrap for ratio metrics to avoid distributional assumptions.
Mention multiple testing correction (e.g., Bonferroni) if testing multiple metrics, and discuss power analysis to ensure adequate sample size.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by decomposing contribution per order into its components (GMV, take rate, delivery cost) and then model each as a function of order volume and discount level. Use a hierarchical or simulation-based approach to capture nonlinearities like delivery cost economies of scale and take rate saturation, then propagate uncertainty via Monte Carlo to inform a decision rule (e.g., expected value with confidence intervals).
Pro tip: Emphasize that the decision should be based on the distribution of outcomes, not just point estimates—use probabilistic sensitivity analysis to identify which parameters most affect the decision, and consider the cost of being wrong (e.g., asymmetric loss).
Clearly state the contribution per order formula and identify each component: GMV, take rate, and delivery cost. Discuss how each might vary with order volume and discount level.
Propose a functional form for delivery cost as a function of order volume (e.g., fixed + variable with economies of scale) and possibly take rate (if discounts affect basket size). Consider piecewise or nonlinear models.
Explain how take rate might saturate at high discounts (e.g., logistic or diminishing returns). Incorporate behavioral assumptions or historical data to parameterize the saturation curve.
Use Monte Carlo simulation or Bayesian methods to combine parameter uncertainty from each component. Generate a distribution of contribution per order under different scenarios.
Define a decision rule (e.g., maximize expected contribution, ensure probability of positive contribution > X%). Perform sensitivity analysis to identify key drivers and recommend actions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I liked this one more than the stats questions.
Start by framing the segmentation as a pre-specified, hypothesis-driven analysis with a limited set of dimensions to avoid data dredging. Use a hierarchical testing strategy or false discovery rate control to manage multiple comparisons, then validate promising segments with a holdout set or follow-up experiment. Prioritize segments based on practical significance (effect size and business impact) rather than just statistical significance.
Pro tip: Pre-register your segmentation plan and primary dimensions before looking at the data; this prevents p-hacking and makes your findings more credible. Also, consider using a Bayesian approach with shrinkage to borrow strength across segments, which naturally controls for multiple comparisons and highlights truly heterogeneous effects.
Select a limited set of theoretically motivated dimensions (e.g., market, time of day, basket size, user type) and specify expected directions of effect. Avoid creating too many segments by combining dimensions unless justified.
Decide on a method such as Bonferroni, Holm, or Benjamini-Hochberg (FDR) based on the number of tests and tolerance for false positives. For exploratory analysis, FDR is often preferred to balance discovery and error control.
Run the analysis for each segment, compute p-values, and apply the chosen adjustment. Also, report effect sizes and confidence intervals to assess practical significance.
Use a holdout sample or a follow-up experiment to confirm findings. Prioritize segments with large, consistent effects and clear business implications, considering implementation costs.
Present results with appropriate caveats about multiple testing. Recommend next steps, such as targeted experiments or deeper dives, and iterate the segmentation plan as more data becomes available.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Shortest answer I gave and probably the weakest.
Frame the problem as a constrained optimization where the objective is to maximize a blended metric (e.g., revenue minus penalties for cancellations) subject to hard service-level constraints. Propose a dynamic, data-driven approach that adapts to real-time conditions, and emphasize the need to validate the model through simulation and A/B testing before deployment.
Pro tip: Highlight the trade-off between short-term revenue and long-term customer trust: cancellations can erode lifetime value, so consider incorporating a penalty for cancellations that reflects their downstream impact. Also, mention that the optimization should be robust to uncertainty in demand and supply forecasts.
Propose a composite objective that balances revenue lift and service-level metrics, such as revenue minus a penalty term for cancellations (e.g., λ * cancellation_rate). The penalty λ should be calibrated based on the business cost of cancellations.
Specify hard constraints like maximum allowable cancellation rate (e.g., ≤ 5%) and minimum shopper utilization, as well as operational constraints like budget for incentives or maximum wait time.
Recommend a method suitable for dynamic, uncertain environments, such as stochastic programming, reinforcement learning, or a bandit approach, depending on data availability and latency requirements.
Outline a validation plan using historical data simulation and online A/B testing, with guardrail metrics to ensure service levels are not compromised. Emphasize continuous monitoring and retraining.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.