Lyft Demand Modeling & Forecasting Questions

Techniques for modeling and forecasting ride-hailing demand, including time-series forecasting, demand drivers, feature engineering, model selection (e.g., ARIMA, Prophet, ML-based predictors), evaluation metrics (MAPE, RMSE), and deployment considerations within analytics workflows for transportation data.

EasyTechnical

69 practiced

Describe trend and seasonality in ride-hailing time series. Explain how you would detect weekly seasonality, daily patterns, and long-term trends in hourly ride counts using exploratory analysis and statistical methods. Mention specific techniques (STL decomposition, ACF/PACF, Fourier terms) and describe scenarios where those methods may fail.

MediumTechnical

72 practiced

Explain probabilistic forecasting for ride-hailing: how to generate quantile forecasts (e.g., 10th, 50th, 90th percentiles) using methods like quantile regression, gradient boosting with quantile objectives, or ensemble bootstrapping; and how to evaluate these forecasts using pinball loss and coverage metrics. Discuss when probabilistic forecasts are necessary for operations.

MediumTechnical

62 practiced

Your production forecasting model's accuracy degraded by about 12% immediately after a new regional pricing policy was introduced. Draft an investigation plan: what data checks and visualizations to run first, how to separate policy impact from seasonality, candidate modeling remedies (retraining, new features, model revamp), and how to communicate findings and mitigation steps to stakeholders.

HardTechnical

55 practiced

You've deployed a new forecasting model that reduces overall MAPE but substantially increases errors in low-income neighborhoods, which negatively affects driver allocations. As BI lead, how would you evaluate the trade-offs, propose remediation steps (short-term and long-term), engage stakeholders, and recommend a path forward balancing forecast accuracy and fairness? Include metrics to track post-remediation.

MediumTechnical

81 practiced

Marketing plans a 20% weekend discount in California. Describe how you would forecast incremental demand attributable to the campaign, how to measure lift (preferred experimental or observational approaches), and outline a dashboard layout showing realized lift, confidence intervals, and ROI for marketing and finance stakeholders.

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