Two Sigma·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Two Sigma data scientist interview with a meaty open-ended question about duplicate detection in transaction data. No fluff, they just dropped a scenario and let you run with it. Felt more like a whiteboard design session than a typical DS screen.

Questions Asked (1)

You have a dataset of credit card transactions and suspect some records are duplicates. How would you define what counts as a true duplicate, identify real-world causes of duplication, and then detect, quantify, and extract both exact and near-duplicate records in a robust way?

Root Cause AnalysisData ModelingTechnical Trade-offs

Author's notes

This one sprawled in a way I wasn't ready for.

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AI HintsAI Generated

Suggested Approach

Start by defining a true duplicate as a record that represents the same real-world transaction, which requires understanding the data model and business context. Then systematically explore causes of duplication, implement detection for exact and near-duplicates using appropriate techniques, and quantify and extract duplicates with clear criteria. Emphasize trade-offs between precision and recall and the importance of domain knowledge.

Pro tip: In interviews, demonstrate that you consider the business impact of false positives and false negatives—e.g., flagging a legitimate transaction as duplicate could harm customer experience. Also, mention that near-duplicate detection often requires a combination of deterministic and probabilistic methods, and that you would validate with domain experts.

1. Define True Duplicate

Clarify what constitutes a true duplicate by considering the business definition: same transaction ID, same amount, timestamp, merchant, and card, but also account for legitimate repeated transactions (e.g., subscriptions). Engage stakeholders to agree on criteria.

2. Identify Causes of Duplication

Explore potential sources: system glitches, double submission, data integration errors, or intentional duplicates. Understand the data pipeline to pinpoint where duplication might occur.

3. Detect Exact and Near-Duplicates

For exact duplicates, use hashing or grouping on all fields. For near-duplicates, define similarity metrics (e.g., Jaccard, Levenshtein, or cosine similarity on embeddings) and set thresholds. Consider blocking or indexing to scale.

4. Quantify and Extract Duplicates

Quantify the extent by counting duplicate groups and estimating impact. Extract duplicates by flagging or removing them, ensuring you preserve a mapping for auditing. Use clustering or graph-based methods for transitive duplicates.

5. Validate and Iterate

Validate results with domain experts and measure precision/recall if labels exist. Iterate on thresholds and features to balance false positives and negatives based on business cost.

Key Points to Mention

Business context and domain knowledge to define duplicates
Data profiling and exploratory analysis to understand duplication patterns
Exact duplicate detection using hashing or grouping
Near-duplicate detection with similarity metrics and thresholds
Scalability considerations (e.g., blocking, approximate nearest neighbors)
Trade-offs between precision and recall and business impact
Validation with domain experts and iterative refinement

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