TD Data Scientist Interview Questions
The questions to prepare for a TD Data Scientist interview. Questions from real interview reports rank first. Updated daily.
Design an A/B test for a new platform feature, including success metrics, power, guardrails, and a clear ship decision.
Balance predictive performance with fairness checks and interpretable explanations when using complex black-box models.
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Best practices for reproducible dataset and model versioning in shared ML pipelines.
Design a real-time fraud scoring system for card transactions with strict latency, delayed labels, and high availability requirements.
Explain what a p-value means, how it relates to statistical significance, and how to describe it clearly to non-technical stakeholders.
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.
Tests SQL proficiency for real-world data extraction and transformation.
Use joins, aggregation, HAVING, and CASE to identify customers with at least two completed orders.
Optimize a PostgreSQL reporting query using selective filtering, aggregation, indexing strategy, and execution-plan reasoning.
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