Capital One·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Capital One data scientist interview that was basically one long technical problem about merging messy CSV files on a memory-constrained machine. Pretty deep in the weeds for a single question, covering everything from encoding detection to chunked streaming to ISO week math.

Questions Asked (1)

You're given four CSV files with inconsistent delimiters, mixed encodings, and possible duplicate header rows. On a laptop with 8 GB RAM and no cloud access, how would you merge them, handle files exceeding 10 million rows, and produce two specific output files: one with minimum purchase price per category (filtered by star rating) and one with weekly watch time per category?

System DesignData ModelingTechnical Trade-offs

Author's notes

This was a beast.

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AI HintsAI Generated

Suggested Approach

Start by profiling the files to understand delimiter, encoding, and header issues, then design a chunked, streaming pipeline that processes each file in manageable pieces, merges and deduplicates on the fly, and writes intermediate results to disk. Finally, perform the two aggregations using memory-efficient techniques like groupby with categorical data types and incremental updates, ensuring the final outputs are written in the required format.

Pro tip: Always validate assumptions early with a small sample and use memory profiling (e.g., pandas' memory_usage) to catch unexpected memory spikes before scaling to full data. Also, consider using a lightweight database like SQLite for intermediate storage to simplify merging and querying without loading everything into RAM.

1. Profile and Clean Input Files

Inspect each CSV to detect delimiter, encoding, and header anomalies using a small sample. Write a robust parser that handles these inconsistencies and skips duplicate headers.

2. Design Chunked Processing Pipeline

Process each file in chunks (e.g., 100k rows) using pandas' chunksize or Dask, applying cleaning and type conversions. Write cleaned chunks to temporary Parquet files for efficient storage and faster subsequent reads.

3. Merge and Deduplicate

Merge the cleaned chunks into a single dataset, either by concatenating Parquet files or using an external merge with SQLite. Deduplicate records based on a unique key (e.g., transaction ID) to avoid double-counting.

4. Compute Required Aggregations

For minimum purchase price per category filtered by star rating, filter and group by category, computing min price. For weekly watch time per category, derive week from timestamp, group by category and week, and sum watch time. Use incremental aggregation if data doesn't fit in memory.

5. Output and Validate Results

Write the two output files in the specified format (e.g., CSV). Validate results by cross-checking with a sample or using summary statistics to ensure correctness.

Key Points to Mention

Handling inconsistent delimiters and encodings with robust parsing (e.g., using csv.Sniffer, chardet).
Memory-efficient techniques: chunking, streaming, using Parquet for intermediate storage, and categorical data types.
Deduplication strategies: identifying unique keys and using drop_duplicates or SQL DISTINCT.
Incremental aggregation for large data: using groupby with chunks and updating running aggregates.
Trade-offs between using pandas, Dask, or SQLite for out-of-core processing on a memory-constrained laptop.
Validation and error handling: ensuring data quality and reproducibility of results.

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