Apple·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Apple data scientist interview that was basically a single monster question disguised as a whole technical round. They handed me this massive random forest from scratch prompt and I spent the better part of an hour trying to keep all the moving parts in my head at once.

Questions Asked (1)

Implement a binary classification Random Forest from scratch for 200,000 samples and 100 mixed features under a 2 GB memory budget. Cover CART with Gini impurity, missing value handling, categorical splits without one-hot encoding, bootstrap and feature bagging with reproducible seeds, OOB evaluation including ROC-AUC and PR-AUC, a reliability diagram, Platt scaling vs isotonic regression calibration, severe class imbalance handling with a 10x FN/FP cost ratio, thread-safe parallelization with complexity analysis, and warm-start/streaming with concept drift detection via OOB metrics. Write pseudocode for train(), predict_proba(), and OOB evaluation and justify every design choice.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I knew random forests reasonably well going in but this question is basically five separate hard questions stapled together.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a modular design that addresses each component (CART, missing values, categorical splits, bagging, OOB, calibration, imbalance, parallelization, streaming). For each component, explain the algorithm, justify design choices, and provide pseudocode for train(), predict_proba(), and OOB evaluation. Conclude with complexity analysis and trade-offs.

Pro tip: Emphasize memory efficiency: use float32, sparse representations, and out-of-core processing where possible. Also, highlight that OOB evaluation can be used for early stopping and drift detection without a separate validation set.

1. Clarify Requirements and Constraints

Restate the problem: binary classification, 200k samples, 100 mixed features, 2GB memory. Confirm assumptions (e.g., feature types, missingness, class imbalance ratio) and discuss trade-offs (e.g., exact vs approximate splits).

2. Design Core Tree Algorithm

Describe CART with Gini impurity, handling missing values via surrogate splits or probabilistic split, and categorical splits using sorted category grouping (e.g., by response rate) without one-hot encoding. Justify choices for memory and performance.

3. Ensemble and Evaluation

Explain bootstrap and feature bagging with reproducible seeds, OOB evaluation including ROC-AUC and PR-AUC, reliability diagram, and calibration methods (Platt scaling vs isotonic regression). Address class imbalance with 10x FN/FP cost ratio via class weights or threshold tuning.

4. Parallelization and Streaming

Discuss thread-safe parallelization (e.g., parallel tree building with locks or atomic operations) and complexity analysis. Cover warm-start/streaming with concept drift detection using OOB metrics (e.g., monitoring OOB AUC over time).

5. Pseudocode and Justification

Provide pseudocode for train(), predict_proba(), and OOB evaluation. Justify every design choice (e.g., why Gini, why surrogate splits, why isotonic regression for calibration). Summarize with complexity and memory analysis.

Key Points to Mention

Memory-efficient data structures: float32, sparse matrices, and out-of-core processing to stay within 2GB.
Handling missing values: surrogate splits or probabilistic split, avoiding imputation to preserve signal.
Categorical splits without one-hot encoding: sorting categories by response rate and finding optimal partition.
Reproducible seeds: setting seeds for bootstrap and feature bagging to ensure reproducibility.
OOB evaluation: using out-of-bag samples for ROC-AUC, PR-AUC, and reliability diagram; calibration with Platt scaling vs isotonic regression.
Class imbalance: cost-sensitive learning with 10x FN/FP ratio, using class weights or threshold optimization.
Parallelization: thread-safe tree building with locks or atomic operations, complexity analysis (O(n log n) per tree).
Streaming and drift detection: warm-start with incremental learning, monitoring OOB metrics for concept drift.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.