TD Data Scientist Interview Questions
The questions to prepare for a TD Data Scientist interview. Questions from real interview reports rank first. Updated weekly.
Explain how bagging and boosting differ, and identify a representative algorithm for each ensemble method.
Explain what a p-value means, how it relates to statistical significance, and how to describe it clearly to non-technical stakeholders.
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Explain which metrics matter for evaluating a churn model and how to choose them based on retention costs and business goals.
Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.
Design a production ML deployment on Google Cloud with serving, feature management, rollout, monitoring, and evaluation.
Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.
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