This is the kind of question that sounds straightforward until you actually start talking through it.
Start by clarifying the problem scope—what constitutes a bot, what data is available, and what the business impact is. Then walk through the ML lifecycle: data collection and labeling, feature engineering, model selection, training, evaluation, and deployment with monitoring. Emphasize trade-offs between precision and recall, latency, and scalability, and how you'd iterate based on feedback.
Pro tip: Frame the problem as an adversarial, evolving one—bots adapt, so your model must be retrained frequently and include anomaly detection for novel patterns. Mention that you'd start with a simple baseline (e.g., logistic regression) to establish metrics before moving to complex models.
Ask questions to understand what type of bots (spam, scraping, fake accounts), the data sources (logs, user behavior, IP), and success metrics (precision, recall, false positive cost).
Identify and gather relevant data, handle labeling challenges (e.g., weak supervision, manual review), and address class imbalance and concept drift.
Design features from behavioral, network, and content signals; choose models balancing interpretability, latency, and accuracy (e.g., gradient boosting, neural networks).
Split data temporally to simulate real-world deployment, use appropriate metrics (PR-AUC, F1), and validate against adversarial examples.
Deploy with A/B testing, monitor for drift and performance degradation, set up feedback loops for continuous retraining, and consider ensemble or online learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.