← Boston Consulting Group Interview Insights

Boston Consulting Group·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

BCG data scientist technical screen, basically one big pandas problem that looked manageable until you actually had to write production-quality code for it. A lot of moving parts crammed into a single question.

Questions Asked (1)

Q1

Given three DataFrames (customers, products, orders), write idiomatic vectorized pandas code that: filters to completed orders and computes revenue, merges the tables with appropriate validation, calculates a clipped discount percentage with median imputation for missing values, identifies each customer's first purchase per model via groupby/transform, enforces correct dtypes (categorical and datetime), avoids SettingWithCopyWarning, and returns a specific set of columns sorted by revenue descending. The solution must be idempotent.

Data ModelingTechnical Trade-offsAlgorithms & Data Structures
Author's notes

This was a lot.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by outlining a clear pipeline: filter, compute, merge, transform, and finalize. Emphasize vectorized operations, proper dtype handling, and idempotency. Walk through each step with code snippets, explaining the rationale behind choices like validation and categorical types.

Pro tip: Use merge with validate='many_to_one' to catch data issues early, and ensure idempotency by avoiding in-place modifications and using .copy() when needed.

1. Filter and Compute Revenue

Filter orders to status 'completed', then compute revenue as quantity * unit_price, ensuring no SettingWithCopyWarning by using .copy() or .loc.

2. Merge with Validation

Merge orders with customers and products using appropriate keys and validate='many_to_one' to ensure data integrity, then check for missing values.

3. Handle Discounts and Imputation

Clip discount percentage to [0, 1], then impute missing values with the median discount using fillna.

4. Identify First Purchase per Model

Use groupby on customer and model, then transform with 'min' on order_date to flag first purchases, ensuring datetime dtype.

5. Enforce Dtypes and Finalize

Convert customer_id and model to categorical, order_date to datetime, select required columns, sort by revenue descending, and reset index for idempotency.

Key Points to Mention

  • Vectorization: avoid loops, use pandas built-in methods
  • Merge validation: prevent unexpected row duplication
  • SettingWithCopyWarning: use .copy() or .loc for assignments
  • Idempotency: ensure function can be run multiple times without side effects
  • Categorical dtypes: memory efficiency and proper handling
  • Datetime conversion: use pd.to_datetime with errors='coerce' if needed

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