The Boston Consulting Group Data Scientist Interview Questions + Guide in 2025

Navigating the Boston Consulting Group (BCG) data scientist interview can be challenging, as it requires a strong understanding of both technical skills and business acumen. BCG is one of the “Big Three” consulting firms, alongside McKinsey & Company and Bain & Company, collectively referred to as “MBB.” BCG is known for delivering innovative solutions through management consulting, technology and design, as well as corporate and digital ventures.

With the rapid growth of BCG X, the firm’s technology and design arm, the demand for data scientists is increasing significantly. BCG X brings together over 3,000 tech experts, providing a dynamic environment for data professionals.

Landing a data scientist role at BCG offers numerous advantages, including access to cutting-edge projects, strong career growth, and competitive compensation. If you’re interested in data science and business impact, it’s one of the places you should be.

This guide will provide you with the BCG data scientist interview process, common interview questions, and preparation tips to help you excel.

BCG X is the tech build and design unit of Boston Consulting Group that brings together technologists, builders, and designers to help organizations enable innovation at scale.

Jessica Apotheker, BCG chief marketing officer and expert leading large-scale digital go-to-market transformations, said, “BCG X is a multiplier of our clients’ technology teams, working in close collaboration, to bring our pool of leading talent in AI, software engineering, digital growth, green and deep tech.”

Before BCG X was launched, BCG had technology-focused divisions, including BCG Gamma, BCG Digital Ventures, and BCG Platinion. BCG Gamma specializes in data science, advanced analytics, machine learning, and AI. Now, it is under the AI & Software umbrella within BCG X. This unit is dedicated to building cutting-edge AI and digital industrial-grade software powered by proprietary data assets to enable better and faster decision-making.

At Boston Consulting Group, data scientists play a vital role in transforming data into business insights. They build advanced models, create algorithms, and use AI to analyze information and improve decision-making. Their responsibilities include:

• Applying data science techniques to solve real-world business problems across industries.
• Collaborating with engineers, researchers, and consultants to develop and implement data-driven solutions.
• Optimizing business processes like supply chains, customer engagement, and recommendation systems through predictive analytics.
• Driving AI-powered transformations to help clients innovate and stay competitive.
• Contributing to thought leadership by writing research papers, presenting at conferences, and engaging with the data science community.

Qualifications

To qualify as a data scientist at Boston Consulting Group, you typically need the following:

• A master’s degree or PhD in computer science, data science, mathematics, statistics, engineering, or a related field.
• At least 1–3 years of relevant work experience applying data science techniques to solve business challenges.
• Proficiency in Python, R, SQL, and other tools for data analysis and machine learning.
• Ability to simplify complex results or processes into simple, clear visualizations.
• Comfortable in a client-facing role with the ambition to lead teams.

The estimated total pay for a data scientist at Boston Consulting Group ranges from $163,000 to $208,000 per year, including base salary and additional compensation. However, this may vary depending on experience and location.

BCG follows a rigorous and challenging process. Once your resume is reviewed and you are selected, the interview process begins and typically takes between 2 and 4 months. Here’s an overview of the typical stages:

1. Screening Call (30 minutes)

   Success Rate: Approximately 70–80%

   Estimated Difficulty Level: Low to Moderate

   This is an initial call where the recruiter will ask you to elaborate on your background, skills, and interest in the role. The recruiter may also discuss the role’s responsibilities and type of work.

2. Coding Test (2 hours)

   Success Rate: Approximately 50–70%

   Estimated Difficulty Level: Moderate to High

   The coding test will be conducted online via CodeSignal, which will evaluate your technical skills and problem-solving abilities. You are expected to remain on camera for the duration of your interview. It typically covers topics such as probabilities and machine learning.

3. Case Interviews (45 minutes each)

   Success Rate: Approximately 40–60%

   Estimated Difficulty Level: Moderate to High

   There will be two rounds of case interviews. The first round is a virtual technical case interview with the principal data scientist, followed by a virtual business case with a BCG consultant. During the case interviews, you will be presented with a real-life business scenario. You will need to analyze the problem presented and work toward a solution using clear and logical thinking, creativity, and collaboration.

4. Partner Round

   Success Rate: Approximately 30–50%

   Estimated Difficulty Level: High

   This is the final stage of the interview process, where you will meet with BCG partners. This stage focuses on assessing your technical skills, business acumen, and overall fit for the role. You will also have the opportunity to pitch yourself and explain why you would be a great addition to their team.

Overall, as you move through the BCG data scientist interview stages, the difficulty level increases, and the success rate becomes more uncertain. It’s vital that you prepare thoroughly for each stage.

Statistics and probability questions demonstrate your quantitative skills and ability to apply statistical concepts to solve real-world problems.

1. How would you interpret coefficients of logistic regression for categorical and Boolean variables?

   When dealing with logistic regression coefficients for categorical and Boolean variables, think of them as log-odds. If the coefficient is positive, that category increases the likelihood of the outcome; if it’s negative, it decreases it.

2. Why do we need time series models when we have less complicated regression models?

   Regular regression assumes independent observations, but time series data has patterns like trends and seasonality that need special handling.

3. What are z- and t-tests, and when should one be used over the other?

   They both test means, but use z-tests when you have a large sample size and known variance, and t-tests when your sample is small and variance is unknown.

4. Describe p-values in layman’s terms.

   You can say that p-values tell you how strong the evidence is against a null hypothesis. A small p-value (like <0.05) suggests your results are likely not due to random chance.

   Tip: You can use simpler terms and even use an analogy to lay out your points better.

5. Explain how a probability distribution could not be normal and give an example scenario.

   Probability distributions aren’t always normal. Imagine trying to predict customer churn—most people stay, but a small number leave suddenly. That’s a skewed distribution.

6. You’re given a biased coin that comes up heads 30% of the time when tossed. What is the probability of the coin landing as heads exactly 5 times out of 6 tosses?

   First, calculate the probability of a single sequence (e.g.,  HHHHHT) using the individual probabilities of heads (0.3) and tails (0.7). Second, determine the number of different ways to arrange the 5 heads and 1 tail using the binomial coefficient. Lastly, multiply the probability of a single sequence by the number of possible sequences.

Machine learning questions evaluate your skills in model building, understanding algorithms, and employing problem-solving techniques.

1. What are the assumptions of linear regression?

   Linear regression has several assumptions, including linearity, normality, and independence. There are many aspects to consider, so it’s important that you know and understand the concepts behind it. Understanding them will enable you to explain them in more detail.

2. What’s the difference between lasso and ridge regression?

   Both prevent overfitting, but lasso (L1) actually eliminates some features (drives coefficients to zero), while ridge (L2) just shrinks them but keeps everything in play.

3. How would you handle the data preparation for building a machine learning model using imbalanced data?

   One approach that you can consider to address imbalanced data is oversampling the minority class, either by creating copies of existing samples or generating synthetic data points.

4. You’re tasked with setting the decision threshold for a default risk model. How would you approach this if the goal is to minimize overall financial loss?

   You can focus on balancing the cost of false negatives against the cost of false positives. A high threshold makes the model more conservative, reducing false positives but increasing false negatives. On the other hand, a low threshold makes the model more lenient, reducing false negatives but increasing false positives.

5. If two features are highly correlated in a random forest, how will both those features appear in a measurement of feature importance?

   When two features are highly correlated in a random forest, the model splits on one feature first, so the second one receives less importance. But if you remove one, the importance of the other increases.

SQL questions assess your ability to manipulate and analyze data effectively.

1. Given a users table, write a query to return only its duplicate rows.

   To find duplicate rows in a users table, you can group by all relevant columns and filter using HAVING COUNT(*) > 1. This ensures that only records that appear more than once are returned.

2. You’re given two tables: employees and managers. Find the names of all employees who joined before their manager.

   You can self-join the employees table to the managers table on manager IDs and filter where employee join dates are earlier than manager join dates.

3. Given a table called employees, get the largest salary of any employee by department.

   Use a query that groups all employees by department and then, for each department, finds the maximum salary among the employees.

4. Given a transactions table with date timestamps, sample every 4th row ordered by the date.

   Assign a unique rank to each transaction based on its created_at timestamp using the ROW_NUMBER() window function. Then, filter these ranked transactions, selecting only those where the row number is divisible by 4 using the modulo operator (%).

5. You need to preprocess a dataset containing millions of mortgage records stored in a relational database. Would you use Python or SQL for this, and why?

   SQL is great for filtering, aggregations, and joins at scale, but if you need complex feature engineering or machine learning, Python is better. Using both is effective for optimizing performance, and it allows you to choose the tool for each part of the process.

Technical and business case study questions are designed to assess your analytical skills, problem-solving abilities, and business acumen.

1. Describe your experience with Python and its libraries for data analysis.

   You can share a project you’ve worked on and emphasize how you utilized Python and its libraries for data analysis. For example, you used pandas for data manipulation, NumPy for computations, and Matplotlib/Seaborn for visualization.

2. How do you optimize SQL queries for performance?

   There are several practices that can optimize SQL queries. You can use indexing, avoid SELECT * and retrieve only necessary columns, and optimize JOIN operations, among others.

3. How would you design an A/B test for a new product feature?

   A/B testing is a very useful tool that can be used to develop better product designs. Make sure you randomly split users, define a clear success metric (like conversion rate), and run the test long enough to get statistical significance.

4. How will you calculate the price elasticity of products?

   Price elasticity measures the responsiveness of the quantity demanded or supplied of a good to a change in its price. To calculate price elasticity, divide the change in demand (or supply) for a product by its change in price.

5. A client wants to expand its retail stores. How would you decide the best locations?

   There are several factors to consider when selecting retail store locations. Try looking at the demographics, competitor presence, foot traffic, and real estate costs.

6. What additional factors should be considered as constraints to improve budget allocation accuracy and effectiveness?

   You can think about constraints like regional demand, supply chain limitations, and pricing strategy.

Behavioral questions assess your fit with the company culture and values, as well as your leadership potential and teamwork skills.

1. Why do you want to join BCG?

   Align your answer with BCG’s culture and mission. Talk about your passion for problem-solving, innovation, and collaboration. You can also mention your appreciation for their investment in the learning and growth of their employees, as well as their commitment to fostering a diverse and inclusive workplace.

2. Tell me about a project in which you had to clean and organize a large dataset.

   Reflect on a project that you considered the most challenging yet successfully completed. Explain how you handled missing values, outliers, duplicates, and inconsistent formats efficiently.

3. When was a time you and a coworker had a disagreement, and how did you handle it?

   Talk about how you handled a situation where you acknowledged your coworker’s perspective while sharing your thoughts about it. Highlight how you maintained professionalism and found common ground.

Here are some tips to help you maximize your chances of getting a data scientist role at BCG:

• Think like a consultant, not just a data scientist. Go beyond technical knowledge and practice structuring business problems.
• Be ready to explain complex models and insights in simple terms using compelling narratives and visualizations.
• Understand how BCG connects AI with business strategy.
• Most importantly, practice your communication skills beforehand and ask for feedback from peers. Take advantage of our mock interviews to boost your confidence.

Explore our website to learn more about the resources we offer. Best of luck with your BCG preparation. You’ve got this!