Boston Consulting Group Data Scientist Interview Questions
Master your tech interview with our curated database of real questions from top companies.
Identify Causes and Solutions for Fashion Profit Decline
Timed Case: Fashion Retail Profit Decline — Diagnose and Recommend Context You are analyzing a fashion retailer whose profit has declined year-over-ye...
Evaluate Campaign Lift with Predictive Analytics and Validation Strategy
Evaluate Marketing Campaign Lift (Weekly SKU-Level, 3 Years) Context You have 3 years of panel data at weekly SKU (and optionally region/store) granul...
Design and sample for credit default prediction
A bank wants a model to predict 90-day credit card default at account-month level for proactive outreach. Class prevalence in production is about 2% d...
Transform messy transactions with pandas
You are given two CSVs. transactions.csv - Columns: txn_id, user_id, ts_iso (ISO8601 with timezone), amount (decimal USD; refunds negative), merchant_...
Summarize impact and lessons from your resume
Give a concise 90-second overview tailored to this role. Then deep-dive one project where you changed a business decision using data: state the object...
Explain AUC, imbalance, losses, and networks
Imbalanced Classification & Regression: ROC/PR, Losses, and Training Strategies You are evaluating a binary classifier and a regression head in a mach...
Build and evaluate imbalanced binary classifier
Take‑home: Imbalanced Binary Classification with Temporal Split, Calibration, and Operating Point Selection Context You are given an event‑level datas...
Unify 7 tables and impute missing values
Using pandas, write a robust function unify_orders(...) that ingests seven dataframes (or CSVs) with possibly inconsistent column casing/whitespace an...
Compute posterior and predictive coin probabilities
Bayesian coin: posterior, prediction, and stopping-time expectation Context - You have two coins and will use the same coin for all flips: - Fair co...
Query top spenders and 7-day growth
Assume 'today' = 2025-09-01. Write a SQL query to: (1) for each model, compute total revenue in the last 7 days (2025-08-26 to 2025-09-01 inclusive) a...
Manipulate and merge DataFrames correctly
Given three pandas DataFrames: customers customer_id, join_date, tier 101, 2025-01-02, gold 102, 2025-02-10, silver 103, 2025-03-05, gold products mod...
Reduce overfitting under constraints
Reduce Overfitting Under Latency Constraints (Tabular Regression) Context (assumed) - You have a tabular regression model with a large generalization ...
Defend MSE over MAE for car prices
Choosing MSE vs. MAE for Car Price Regression (Unscaled USD Target) You are training a regression model to predict car prices in USD. The target varia...
Achieve 0.95 precision via thresholding
Deploying a High-Precision Classifier on an Imbalanced Dataset You are given a binary classification problem with 50,000 samples and ~5% positives. Th...
Build a leak-free sklearn pipeline
Take-home: Imbalanced Binary Classification Pipeline with scikit-learn You are training a binary classifier on tabular data with the following feature...
Transform and aggregate messy event data
Using pandas (vectorized; no loops), clean, combine, and aggregate the following to produce country/plan day-level metrics for 2025-08-31. DataFrames ...
Explain AUC, activations, ensembles, and imbalance
Machine Learning Metrics and Modeling Choices — Multi-part You are given model scores and binary labels for a small dataset and asked to compute ROC A...
Compute averages and binomial/Poisson probabilities
Streaming Mean and Binomial vs Poisson Approximation Part A — Streaming Mean Update You have an existing dataset of N = 1,000 observations with mean 1...
Merge and Concatenate Inconsistent Order Files with Pandas
orders_2023 +----------+-------------+--------+ | order_id | customer_id | amount | +----------+-------------+--------+ | 101 | C001 | 120...
Scale and Normalize: When to Use Each Method?
Feature Scaling Before Modeling (CodeSignal Notebook) Context You're preparing features in a notebook step before training a model. You have a pandas ...