The kind of question where you nod confidently and then realize halfway through your answer that you're just listing loss functions instead of actually explaining the decision process.
Start by clarifying the problem type (classification, regression, ranking, etc.) and the business objective, then map that to a loss function that aligns with the evaluation metric. Discuss trade-offs like robustness to outliers, class imbalance, and optimization properties, and mention how you might experiment with alternatives.
Pro tip: At Netflix, many problems are recommendation/ranking tasks, so mentioning pairwise or listwise losses (e.g., BPR, WARP, LambdaRank) and how they connect to business metrics like engagement or retention will stand out. Also, emphasize that the loss function should be differentiable and computationally efficient for large-scale data.
Determine if it's classification, regression, ranking, or something else, and what the model outputs (probabilities, scores, etc.).
Choose a loss that directly optimizes or closely approximates the business/evaluation metric (e.g., cross-entropy for log loss, MSE for RMSE).
Account for class imbalance, outliers, noise, and dataset size; pick losses robust to these (e.g., focal loss for imbalance, Huber for outliers).
Ensure the loss is differentiable, stable, and scalable; consider computational cost and ease of implementation.
Test candidate losses via offline evaluation and A/B tests, monitoring both the loss and the ultimate business metric.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.