The target definition part felt obvious until I actually wrote it out and realized I needed to be careful about what timestamp to anchor on.
Start by precisely defining the prediction target, specifying the exact time point and event being predicted, and clarify the unit of analysis (e.g., per delivery). Then, systematically enumerate features across demand, supply, and network signals, ensuring each feature is actionable and relevant to ETA prediction. Conclude by briefly explaining how these features might be engineered and their potential impact on model performance.
Pro tip: Emphasize that the target should be defined from the customer's perspective (e.g., time from order placement to delivery) and that features must be available at prediction time to avoid data leakage. Mentioning real-time vs. historical feature availability shows practical maturity.
Specify the exact event and time horizon: e.g., predicted delivery time (timestamp) or remaining time from order placement to delivery. Clarify if it's per order, per dasher, or per delivery segment.
List features related to order volume, timing, and customer behavior, such as order timestamp, day of week, historical order volume in area, and order complexity (e.g., number of items).
List features related to dasher availability and performance, such as number of active dashers nearby, dasher historical speed, current dasher workload, and dasher acceptance rate.
List features related to the delivery network and environment, such as distance between restaurant and customer, estimated preparation time, traffic conditions, weather, and road network complexity.
Confirm all features are available at prediction time (no leakage) and discuss potential transformations (e.g., rolling averages, time since last order) to enhance predictive power.
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Leakage is the kind of thing where you nod along and then miss something obvious.
Start by systematically identifying leakage sources in the ETA dataset, focusing on features that would not be available at prediction time or that encode future information. Then, design a time-based cross-validation strategy that respects temporal order, with explicit train, validation, and test splits that mimic production deployment. Emphasize the importance of preventing leakage to ensure model generalizability and discuss trade-offs between different splitting approaches.
Pro tip: Mention that leakage can also occur through data preprocessing steps like target encoding or imputation if done before splitting; always fit these on the training set only. Additionally, highlight that for DoorDash, delivery ETAs are often predicted at order placement, so any post-placement data (e.g., actual delivery time, courier reassignments) must be excluded.
Examine all features and labels for temporal leakage, such as using future data (e.g., actual delivery duration) or features computed after the prediction time (e.g., courier location after assignment). Also check for leakage in data preprocessing and feature engineering.
Clearly specify the moment when the ETA prediction is made (e.g., at order placement) and list all data that would be available at that time. This sets the boundary for legitimate features.
Split the data chronologically into train, validation, and test sets. For example, use the earliest 70% for training, the next 15% for validation, and the most recent 15% for testing. Ensure no shuffling and that splits are contiguous in time.
For hyperparameter tuning, use rolling or expanding window cross-validation on the training set, always validating on future data. This mimics retraining and deployment cycles.
After training, check for suspiciously high performance and compare validation metrics to test metrics. If validation performance is much higher, it may indicate leakage. Also, simulate production by ensuring all preprocessing is fit only on training data.
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I knew pinball loss for quantile regression but fumbled explaining why you'd pick Huber over MAE in plain terms.
Start by framing the problem: P50 and P90 ETA estimation require different modeling considerations. Compare gradient boosting (e.g., LightGBM with quantile loss) and quantile regression (linear or regularized) in terms of flexibility, interpretability, and performance. Justify your choice based on business needs, data characteristics, and evaluation metrics.
Pro tip: Emphasize that quantile regression directly models the conditional quantile, while gradient boosting with quantile loss can capture complex nonlinear relationships. Mention that for P90, the asymmetric loss and tail behavior matter more, so gradient boosting often wins, but quantile regression is a strong baseline and more interpretable.
Discuss the need for P50 (median) and P90 (90th percentile) ETA estimates, and how they are used (e.g., customer promises, driver incentives). Highlight that P90 requires modeling the tail, which is more challenging.
Contrast gradient boosting (e.g., LightGBM, XGBoost) with quantile regression. For gradient boosting, specify using quantile loss (pinball loss) to directly optimize the desired quantile. For quantile regression, mention linear models with L1 or L2 regularization, possibly with splines or interactions.
Discuss flexibility vs. interpretability, training time, scalability, and performance on large datasets. Gradient boosting can capture nonlinearities and interactions but may overfit and is less interpretable. Quantile regression is simpler, faster, and more interpretable but may underfit complex patterns.
Explain that the pinball loss (quantile loss) is appropriate for both approaches when targeting specific quantiles. For P50, it reduces to MAE; for P90, it penalizes underestimation more. Justify based on asymmetric costs: underestimating ETA may be worse than overestimating for customer satisfaction.
Recommend gradient boosting with quantile loss for its flexibility and ability to model complex patterns, but suggest starting with quantile regression as a baseline. Emphasize validation using pinball loss and calibration plots, and consider business metrics like late delivery rate.
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Start by framing ETA prediction as a regression problem where both accuracy and calibration matter for user trust and operational decisions. Then define metrics that capture overall error, tail performance, and calibration, and explain how to construct calibration plots and compute calibration error with binning or smooth methods. Emphasize the business impact of miscalibration, such as underestimating delivery times leading to customer dissatisfaction.
Pro tip: Highlight that calibration should be evaluated conditionally on important segments (e.g., time of day, restaurant type, distance) because aggregate calibration can hide systematic biases that affect specific user groups. Also, mention that for ETA, asymmetric costs mean that over-prediction vs under-prediction may have different business impacts, so calibration alone isn't enough—consider decision-aware metrics.
Cover standard regression metrics like MAE, RMSE, and MAPE, but also include quantile losses (e.g., pinball loss) to assess performance across the distribution. Discuss business-specific metrics such as on-time delivery rate or percentage of predictions within a tolerance window.
Define calibration for ETA as the conditional mean of actual delivery times given predicted ETA being equal to the predicted value. Discuss that perfect calibration means predicted ETAs match observed frequencies across the range.
Describe binning predictions into quantiles or equal-width bins, then plot the mean predicted ETA against the mean actual ETA for each bin. Include a diagonal reference line; deviations indicate miscalibration. Mention alternatives like smooth calibration curves using loess or isotonic regression.
Explain metrics like Expected Calibration Error (ECE) or Mean Absolute Calibration Error (MACE) by taking a weighted average of the absolute differences between mean predicted and mean actual within bins. Discuss the impact of binning choices and potential biases.
Emphasize that calibration should be checked across segments (e.g., peak vs off-peak, cuisine type) to ensure fairness and reliability. Discuss how miscalibration affects customer trust and operational efficiency, and suggest monitoring calibration over time.
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This was the part I found most interesting.
Start by mapping ETA prediction errors to concrete business costs like late deliveries, customer compensation, and courier inefficiencies. Then propose a cost-sensitive objective that directly minimizes expected asymmetric costs, and discuss post-hoc threshold adjustments to balance over- and under-prediction penalties. Emphasize validation with business metrics and iterative refinement.
Pro tip: Quantify the asymmetry: a 5-minute late ETA often costs far more than a 5-minute early ETA, so weight errors accordingly. Also, consider that post-hoc thresholds can be tuned per market or time-of-day to adapt to varying cost structures.
Identify how early vs. late ETA errors impact key metrics such as customer satisfaction, refunds, courier wait time, and support contacts. Assign monetary values to each to quantify asymmetric penalties.
Formulate a loss function that weights errors by their business cost, e.g., asymmetric squared error or quantile loss. Train models to minimize this expected cost directly.
After model training, adjust prediction thresholds (e.g., add a buffer) to minimize expected cost on a validation set, accounting for the asymmetry.
Simulate or A/B test the cost-sensitive model against the current system, measuring impact on delivery times, refunds, and customer ratings.
Continuously monitor cost parameters and model performance, retraining as business conditions change to maintain optimal cost balance.
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SHAP auditing I've done before so this felt comfortable.
Start by framing the audit as a fairness evaluation of the ETA model across protected groups (neighborhoods, vehicle types). Then explain how you'd use SHAP or permutation importance to identify biased features and quantify their impact, and finally propose mitigations like reweighting, feature removal, or post-processing calibration.
Pro tip: Emphasize that bias audits should be continuous and tied to business metrics like delivery time accuracy and customer satisfaction, not just statistical parity. Also, mention that SHAP values can reveal proxy features (e.g., neighborhood as a proxy for race) that permutation importance might miss due to correlations.
Identify the groups to audit (e.g., neighborhoods by demographic composition, vehicle types) and choose fairness metrics such as disparate impact, equal opportunity, or predictive parity. Align these with DoorDash's business goals.
Use SHAP to get global and local feature importance, and permutation importance to validate. Compare importance across groups to detect if certain features (e.g., neighborhood) disproportionately affect predictions for specific groups.
Investigate whether biased features are legitimate (e.g., distance) or proxies for protected attributes. Use SHAP dependence plots and interaction effects to understand how features like vehicle type interact with neighborhood to produce bias.
Choose mitigations based on bias source: pre-processing (reweighting, resampling), in-processing (fairness constraints, adversarial debiasing), or post-processing (calibrating predictions per group). Consider trade-offs with model accuracy and business metrics.
Set up continuous monitoring of fairness metrics and feature importance drift. A/B test mitigations to ensure they reduce bias without harming user experience, and iterate as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
System design questions for ML always feel like they could go on forever.
Start by clarifying the business context and requirements, then walk through the end-to-end system design covering data flow, model serving, and monitoring. Emphasize trade-offs and how you would validate the system through online experiments.
Pro tip: Demonstrate awareness of the cold-start problem for new restaurants and the importance of fallback strategies to maintain user experience. Also, highlight how you would measure business impact beyond model metrics.
Ask questions to understand scale (e.g., QPS, number of restaurants), latency SLAs, data freshness needs, and business goals. This ensures your design is tailored to DoorDash's specific context.
Outline how features are computed, stored, and served. Distinguish between batch and streaming features, and explain how you ensure low-latency access and consistency between training and serving.
Describe how the model is deployed for real-time inference, including latency requirements, scaling, and fallback mechanisms. Mention techniques like model quantization or caching to meet SLAs.
Explain the retraining cadence (e.g., daily/weekly) and how you monitor for data drift, concept drift, and performance degradation. Include automated alerts and retraining triggers.
Detail how you would A/B test the model, including metrics (e.g., ETA accuracy, delivery time, user engagement), experiment duration, and guardrail metrics. Discuss phased rollout and monitoring.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.