I went straight to multiclass classification and the interviewer pushed back a bit, asking what the dispatcher actually consumes.
Start by clarifying the business objective: is the goal to optimize elevator dispatch (which benefits from a per-floor arrival rate model) or to predict the exact next floor (a classification problem)? Then discuss how the problem framing depends on the decision to be made, and propose a hybrid approach that models arrival rates per floor and time, which can be converted into a classification if needed.
Pro tip: Emphasize that in real-world systems, the cost of misclassification varies by floor (e.g., predicting a high-traffic floor incorrectly is worse), so a probabilistic model that outputs calibrated probabilities is often more useful than a hard classification.
Ask whether the prediction is for optimizing elevator dispatch (e.g., pre-positioning cars) or for a user-facing feature (e.g., showing the next likely floor). This determines whether you need a point prediction or a probability distribution.
Identify available data: historical elevator call logs, time of day, day of week, building occupancy patterns, and special events. Recognize that arrivals are often non-homogeneous Poisson processes with time-varying rates.
Compare classification (multiclass over floors) vs. per-floor arrival rate models (e.g., Poisson regression, survival analysis). Discuss that classification ignores the timing and rate, while rate models capture the stochastic nature and can be used to derive probabilities.
Suggest modeling per-floor arrival rates as a function of features (time, day, etc.), then normalizing rates to get a probability distribution over floors. This gives both the rate and a classification if needed, and allows for cost-sensitive decisions.
For rate models, use likelihood or Poisson deviance; for classification, use log-loss or top-k accuracy. Also consider business metrics like average wait time reduction or dispatch efficiency.
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Time-of-day and day-of-week are obvious, but I made sure to talk about cyclical encodings rather than raw hour integers.
Start by clarifying the business objective and defining the prediction target (e.g., which floor a call originates from). Then outline the data sources (historical call logs, building layout, time, weather) and features (temporal, spatial, behavioral) that could be predictive. Finally, discuss how you would validate feature importance and iterate to identify which features actually drive the prediction.
Pro tip: Emphasize that feature importance is context-dependent and must be validated with both statistical methods (e.g., permutation importance) and business impact (e.g., A/B testing). Also, mention that simple features like time of day and floor proximity often outperform complex ones, so start simple and add complexity only if needed.
Define what 'elevator call origins' means: is it predicting the floor where a call button is pressed, or the direction (up/down)? Understand the business use case (e.g., optimizing elevator dispatch) to guide feature selection.
List available data: historical elevator call logs (timestamp, floor, direction), building metadata (floor plans, tenant locations), external data (weather, events), and sensor data (occupancy, motion).
Create features from raw data: temporal (hour, day of week, holidays), spatial (floor number, distance to common areas), historical (frequency of calls per floor, time since last call), and contextual (weather, events).
Use models like random forests or gradient boosting to compute feature importance. Validate with permutation importance and SHAP values to understand which features drive predictions.
Test the model's performance with cross-validation and possibly online A/B testing. Iterate on features based on performance and business feedback, removing noisy or redundant features.
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Label leakage question dressed up in domain clothing.
Start by clarifying the prediction task and the temporal nature of the data, then describe a point-in-time correct feature engineering pipeline that only uses data available up to each prediction timestamp. Emphasize how you would validate the approach with time-based splits and monitor for leakage.
Pro tip: Use event-time processing with watermarks to handle late-arriving data, and always log the exact feature values used at prediction time to enable reproducibility and leakage audits.
Clarify what you are predicting (e.g., next call floor, wait time) and the exact prediction time for each training example. This determines which past data is available.
For each training example, compute features using only events with timestamps strictly before the prediction time. Use windowed aggregations (e.g., last 5 minutes, last hour) and ensure windows are closed.
Use a framework like Apache Flink or Spark Structured Streaming with event-time processing and watermarks to handle out-of-order data. Alternatively, simulate with batch processing that respects event time.
Split data chronologically (train on past, validate on future) and check for leakage by comparing offline metrics with online performance. Use techniques like backtesting.
Log feature values at prediction time and set up alerts for distribution shifts. Periodically audit the pipeline to ensure no future data is inadvertently used.
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Top-1 accuracy felt like the wrong answer and I said so upfront.
Start by outlining a rigorous offline evaluation plan that includes appropriate metrics, validation strategies, and baselines. Then, connect those offline metrics to the business objective, emphasizing that the ultimate measure is the online impact on key business KPIs. Finally, discuss how you would validate the offline-online relationship and iterate.
Pro tip: At Amazon, always tie your model evaluation to a business metric like revenue, customer acquisition cost, or Prime member engagement, and mention how you would design an A/B test to confirm offline gains translate online.
Clarify the business problem the model addresses and identify the primary business KPI (e.g., conversion rate, revenue per user, customer lifetime value). Then, select offline metrics that are proxies for that KPI.
Select metrics based on the model type (e.g., AUC, precision@k, RMSE) and ensure they align with the business goal. Consider multiple metrics to capture different aspects (e.g., accuracy and fairness).
Use techniques like cross-validation, time-based splitting, and holdout sets to avoid overfitting and simulate production conditions. Compare against baselines and consider confidence intervals.
Explain how offline improvements are expected to translate to online gains. Discuss potential gaps (e.g., feedback loops, user behavior) and how to mitigate them.
Propose an A/B test to measure the true business impact. Define success criteria, sample size, and duration. Use results to refine the model and offline metrics.
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Start by reframing 'good enough' as a business decision tied to the cost of errors and the availability of idle cars. Explain that with multiple idle cars, top-3 hit rate may be sufficient because the system can offer alternatives, but you must quantify the trade-off between accuracy and utilization. Then propose how to use the model output: rank cars, set a confidence threshold, and possibly use top-3 as a fallback, while monitoring business metrics like conversion and idle time.
Pro tip: Tie your answer to Amazon's leadership principles like Customer Obsession and Dive Deep: show that you'd validate the top-3 approach with an A/B test measuring customer experience and fleet efficiency, not just offline accuracy.
Ask or state assumptions about the goal: is it to maximize successful matches while minimizing idle car time? Identify the cost of a wrong top-1 prediction versus the cost of not using an idle car.
Propose that 'good enough' depends on whether top-3 can achieve the desired outcome (e.g., high match rate) with acceptable efficiency. If multiple idle cars exist, top-3 may be sufficient because the system can offer alternatives.
Suggest using top-3 as a ranked list: present the top choice first, but if it's unavailable or rejected, fall back to the next. Set a confidence threshold to decide when to use top-1 versus top-3.
Propose an A/B test comparing top-1-only vs. top-3 fallback, measuring business metrics like conversion, idle time, and customer satisfaction. Use offline metrics as a sanity check but rely on online results.
Recommend continuous monitoring of top-3 hit rate and business KPIs, and retraining the model if the distribution of idle cars changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the cold-start problem and the need for a hybrid approach that leverages transfer learning from similar buildings and incorporates any available exogenous data. Then describe a concrete day-one model, such as a hierarchical Bayesian model or a gradient-boosted tree with engineered features, and explain how it produces predictions with uncertainty. Finally, outline a clear adaptation strategy, like online learning or periodic retraining, that gradually shifts reliance from priors to the building's own data as it accumulates.
Pro tip: Emphasize that you would quantify and communicate uncertainty in day-one predictions to stakeholders, and set up automated monitoring to detect when the model's performance degrades or improves, triggering retraining. This shows you think about the full lifecycle and business impact, not just the algorithm.
Ask about the prediction target (e.g., call volume, duration), available data (e.g., building metadata, similar buildings, external signals), and business requirements (e.g., latency, accuracy). This ensures you design a fit-for-purpose solution.
Use data from similar buildings or domains to build a prior model. For example, a hierarchical Bayesian model can pool information across buildings, or you can fine-tune a pre-trained model on the new building's limited data.
Include building-specific attributes (e.g., size, location, occupancy) and time-based features (e.g., day of week, holidays) to make predictions more accurate even without historical call data.
Implement online learning or periodic retraining that gradually increases the weight of the building's own data as it arrives. Use techniques like Bayesian updating or incremental learning to avoid catastrophic forgetting.
Set up metrics to track prediction accuracy and model confidence over time. Use A/B testing or shadow deployment to compare the adaptive model against the day-one baseline, and retrain when performance drops.
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Frame the problem as a decision-making under uncertainty: quantify the uncertainty in demand forecasts and incorporate it into the staging policy via probabilistic constraints or risk-sensitive objectives. Propose a concrete method (e.g., Bayesian models, conformal prediction) and explain how it reduces harmful moves by acting only when confidence exceeds a threshold.
Pro tip: Emphasize that avoiding harmful moves is often more valuable than chasing marginal gains—use a conservative threshold and monitor false-positive rates. Tie your approach to Amazon's leadership principles like 'Customer Obsession' and 'Deliver Results' by showing how uncertainty-aware staging improves reliability and reduces cost.
Use probabilistic forecasting (e.g., quantile regression, Bayesian neural networks) to estimate the distribution of mid-day demand per floor, not just point estimates.
Model the cost/benefit of a staging move as a function of demand uncertainty, e.g., expected cost = P(demand < threshold) * penalty. Set a risk tolerance based on business impact.
Adopt a chance-constrained or robust optimization approach: only trigger a move if the probability of benefit exceeds a confidence level (e.g., 95%). Alternatively, use a Bayesian decision rule that minimizes expected regret.
Simulate the policy on historical data with uncertainty, and A/B test in production. Continuously monitor false-positive rates and adjust thresholds to avoid harmful moves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.