Explain an ML project end-to-end with tradeoffs
Company: Roblox
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Onsite
Pick one of your production ML projects and walk through it end-to-end. Be specific: 1) Problem framing (prediction vs causal decisioning), target definition, and how you prevented label leakage; 2) Data sources, sampling window, and offline metric(s) with rationale (e.g., AUC vs calibration/Brier for monetization); 3) Feature engineering, handling sparse/categorical signals, and how you enforced privacy/fairness constraints; 4) Model choices and tradeoffs (e.g., XGBoost vs shallow nets vs GLM), hyperparameter strategy, and ablations you ran; 5) Error analysis and post-deployment monitoring (drift, stability, guardrail metrics); 6) How you translated model lifts into product impact without an A/B test (e.g., causal uplift modeling, CUPED, backtests); 7) What you would change on a v2 if given twice the data or stricter latency limits.
Quick Answer: This question evaluates a candidate's competency in end-to-end machine learning system design and delivery, covering problem framing, target definition and label leakage prevention, data and metric selection, feature engineering with privacy and fairness constraints, model choice trade-offs, hyperparameter and ablation analysis, and post-deployment monitoring and impact quantification. It is commonly asked to assess practical production experience and trade-off reasoning in the Machine Learning domain, testing both practical application and conceptual understanding of modeling, evaluation, and operational constraints.