← BCG Gamma Interview Insights
Start by clarifying the business objective and defining the prediction window and observation window. Then, systematically engineer features from the transaction data across recency, frequency, monetary, and product preference dimensions, ensuring they are computed only from data available before the prediction period to avoid leakage.
Pro tip: Always discuss how you would handle the temporal aspect: use time-based splitting for validation and ensure features are point-in-time correct. This shows you understand real-world deployment challenges.
Confirm the prediction horizon (next 3 months) and define the observation window (e.g., past 12 months) for feature engineering. Establish the target variable: whether a client makes at least one purchase in the next 3 months.
Compute recency (days since last purchase), frequency (number of transactions), and monetary (total/average spend) for each client. Consider multiple time windows (e.g., last 30, 90, 180 days) to capture different behavioral patterns.
Derive features from item color, such as the proportion of purchases in each color category, color diversity, and whether the client has a dominant color preference. These can indicate taste and potential future purchases.
Calculate inter-purchase time, purchase frequency trends (e.g., increasing/decreasing), and seasonality indicators (e.g., month of last purchase). These help capture customer lifecycle and timing effects.
Use time-based splitting to validate the model and ensure all features are computed using only data prior to the prediction period. Discuss potential leakage and how to prevent it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that suspiciously high accuracy often signals data leakage or evaluation flaws. Then systematically walk through potential causes, covering data issues, methodology errors, and model validation problems. Conclude by emphasizing the importance of rigorous validation and cross-checking with business logic.
Pro tip: Always validate model performance against a simple baseline and inspect feature importance for leakage indicators. If a feature is too predictive, question its availability at prediction time.
Examine whether any features inadvertently contain information about the target that would not be available at prediction time. Common sources include target encoding, time-based leakage, or duplicated rows.
Ensure the data was split correctly (e.g., no overlap, proper stratification, time-based split if needed). Verify that cross-validation folds are independent and that preprocessing steps are fit only on training data.
Double-check that the accuracy metric is computed correctly and appropriate for the problem (e.g., not misleading for imbalanced classes). Look for bugs in code, such as incorrect label mapping or data shuffling errors.
Look for issues like duplicate records, mislabeled data, or features that are proxies for the target. Check if preprocessing (e.g., scaling, imputation) was applied consistently and without leakage.
Compare model performance to simple baselines (e.g., majority class) and domain expectations. If the model vastly outperforms, investigate further; also consider if the problem is too easy or the dataset is too small.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran through the usual: kernel choice, regularization parameter C, feature scaling since SVMs are sensitive to that.
Start by diagnosing the root cause of poor SVM performance through error analysis and validation curves, then systematically address data quality, feature representation, and hyperparameter tuning. Prioritize high-impact fixes like kernel selection and class imbalance before moving to ensemble methods or alternative algorithms.
Pro tip: Always establish a baseline with a simple model (e.g., logistic regression) to confirm whether the issue is SVM-specific or data-related. At BCG Gamma, interviewers value a structured, hypothesis-driven approach over jumping to complex solutions.
Perform error analysis: examine confusion matrix, precision/recall, and misclassified examples to identify patterns (e.g., class imbalance, outliers, non-linear boundaries). Plot learning curves to check for high bias or high variance.
Clean data (handle missing values, outliers), engineer new features, and scale features (critical for SVM). Address class imbalance with techniques like SMOTE, class weights, or resampling.
Optimize C (regularization), kernel type (linear, RBF, polynomial), and kernel-specific parameters (gamma, degree) using grid search or Bayesian optimization with cross-validation.
If performance still lags, try ensemble methods (e.g., bagging SVMs), alternative algorithms (e.g., gradient boosting, neural networks), or dimensionality reduction (PCA, feature selection).
Use nested cross-validation to avoid overfitting during hyperparameter tuning. Compare against baseline models and iterate based on business metrics (e.g., ROI, accuracy thresholds).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.