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Capital One·Data Scientist·Take-home Assignment·Intermediate

Intermediate
May 2026

Summary

Capital One take-home for a Data Scientist role, built around a credit card transactions dataset. The challenge covered SQL aggregations and Python feature engineering, which sounds manageable until you're actually staring at it at 11pm.

Questions Asked (2)

Q1

Using a transactions table with customer IDs, amounts, dates, and merchant categories, write SQL to compute each customer's total and average monthly spend for 2023.

Product Analytics & MetricsData Modeling
Author's notes

My first instinct was a plain GROUP BY on customer and month, which works, but I almost forgot to extract the month from the date field properly and nearly handed in something that grouped by the full date.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the table schema and business definitions (e.g., whether 'average monthly spend' means average over all months in 2023 or only months with transactions). Then write a SQL query that filters for 2023 transactions, aggregates total spend per customer, and computes average monthly spend using a subquery or window function. Finally, validate the results and discuss edge cases like customers with no transactions.

Pro tip: Mention that you would confirm whether to include customers with zero transactions in 2023 (e.g., using a LEFT JOIN from a customer dimension table) and how to handle partial months, as these details often matter in production analytics.

1. Clarify requirements and schema

Ask about the table structure, definitions of 'monthly spend' (e.g., calendar month vs. rolling 30-day), and whether to include customers with no transactions. Confirm the date range and any filters.

2. Filter and aggregate monthly spend

Write a subquery to filter transactions for 2023, extract the month, and sum amounts per customer per month. This gives monthly totals.

3. Compute total and average monthly spend

From the monthly aggregates, compute each customer's total spend (sum of monthly totals) and average monthly spend (total divided by number of months with transactions, or by 12 if including zero months).

4. Handle edge cases and validate

Consider customers with no transactions (use LEFT JOIN or COALESCE), and verify results with sample data. Discuss how to handle partial months or missing data.

Key Points to Mention

  • Use DATE_TRUNC or EXTRACT to group by month.
  • Filter with WHERE transaction_date >= '2023-01-01' AND transaction_date < '2024-01-01'.
  • Decide whether to divide by 12 or by the number of active months for average monthly spend.
  • Use a subquery or CTE to first aggregate monthly totals, then compute overall metrics.
  • Consider using window functions like SUM() OVER (PARTITION BY customer_id) for total spend.
  • Mention performance considerations: indexing on date and customer_id, and avoiding unnecessary columns.

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

Q2

In Python, engineer features that summarize a customer's spend broken down by merchant category, and prepare a modeling-ready dataset from the transactions data.

Data ModelingAlgorithms & Data Structures
Author's notes

This is where I spent most of my time.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the data schema and business objective, then engineer features that capture spend patterns per customer across merchant categories using aggregation and pivot operations. Finally, handle missing values, encode categorical variables, and split the data into training and validation sets to produce a modeling-ready dataset.

Pro tip: Mention that you would create both absolute and relative spend features (e.g., total spend and share of wallet per category) because relative features often generalize better and are more interpretable to business stakeholders.

1. Understand the Data and Objective

Inspect the transactions dataset to identify columns like customer ID, merchant category, transaction amount, and date. Clarify the prediction goal (e.g., churn, next purchase) to guide feature design.

2. Aggregate Spend by Category

Group transactions by customer and merchant category, then compute summary statistics such as total spend, average transaction value, transaction count, and recency. Use pivot tables to create a wide feature matrix.

3. Derive Advanced Features

Create relative features like spend share per category, spend volatility (standard deviation), and trend over time. Consider time-windowed aggregations (e.g., last 30/90 days) to capture temporal patterns.

4. Clean and Encode

Handle missing values (e.g., fill with 0 for no spend), encode categorical variables (one-hot or target encoding), and scale numerical features if needed for the chosen model.

5. Prepare Modeling Dataset

Merge features with the target variable, split into train/validation/test sets, and ensure no data leakage. Optionally, use pipelines to streamline preprocessing.

Key Points to Mention

  • Use pandas groupby and pivot_table for efficient aggregation.
  • Create both absolute and relative spend features (e.g., total spend and percentage of total).
  • Handle missing values appropriately—zero spend may be meaningful.
  • Consider time-based features like recency, frequency, and monetary value (RFM).
  • Encode categorical variables and scale features as needed for the model.
  • Avoid data leakage by computing features only from training data when using time-based splits.

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