BCG Data Scientist Interview Questions + Guide in 2024

Boston Consulting Group (BCG) continues as one of the three most prestigious and influential consulting firms (along with McKinsey and Bain), increasing in revenue and global workforce numbers in 2023. Its subsidiary, BCG X (formerly BCG Gamma), offers clients the latest digital capabilities, from data analytics to artificial intelligence and machine learning.

If you’re preparing for a data scientist interview at BCG X, you’ve come to the right place. We’ve put together a comprehensive guide to help you prepare for the interview stages. Read on to learn more about each step, commonly asked questions, and our favorite tips to get selected! We’ve also compiled links to some valuable resources.

A BCG X data scientist’s role is different from working at a tech company or as a data scientist in a corporate environment. The position usually involves more travel and face time with clients. There are also several routes you can take; some data scientists function like traditional consultants, while others serve as specialists in their areas of expertise.

Most of the interview rounds will be data-intensive case studies. You’ll be tested on coding, machine learning, and your product and business sense. It will be like a consulting case interview that tests your data science knowledge and cultural fit.

The process is usually lengthy and can take 2–4 months to wrap up.

Step 1: Recruiter Screening

After initial contact with the recruiter by email, you’ll either have a phone call with them or a one-way recorded video interview on SparkHire. This round is for assessing fit, and you can expect questions like:

• What makes a great team?
• Why do you want to join BCG? What do you know about BCG?
• What personal traits are necessary for success in consulting?

For the SparkHire option, you won’t be timed as you prepare, but each video answer is limited to 2–3 minutes.

Step 2: Coding Test

Next, you’ll be asked to complete a coding assessment on HackerRank or CodeSignal. This will be a 2-hour-long proctored test (you’ll need to have video and audio on at your end), consisting of 9 questions in Python and R. This test will test your data aggregation, preprocessing, and basic modeling knowledge (such as linear regression).

Step 3: Case Interviews

Once you pass the coding test, you’ll be invited to a series of technical and consulting case interviews. You’ll talk to data scientists from different teams. Typically, two rounds are more interviewee-led, while the interviewer leads the other two. The data scientists will comprehensively assess your knowledge of data science, statistics, probability, and ML and your ability to contextualize these skills in different business domains. Each round will be around 45 minutes long.

Our tip for this round is to practice in the style of traditional consulting case interviews and follow the ASTAR(E) framework to answer: first, provide a high-level Answer to the question. Then, discuss your solution using the STAR method. Finally—though not always essential—talk about the Effect of the business solution or your learnings.

Step 4: Partner Round

The final step in the process is one or two meetings with BCG partners. This stage will focus more on your cultural fit and experience. You will also be asked to present a case for yourself regarding why you should be hired, so prepare a brief elevator pitch highlighting what you’ll bring to the table based on your skill sets and past achievements.

You can have a look at our guides on how to prepare for a data science interview or a data science manager role for more senior/leadership positions.

Let’s examine the top questions asked by BCG X in their coding test, HR round, and data science interviews. Before looking at the solutions, try to solve the questions on your own.

Remember that the interviewer’s main objective is to gauge how well you use various data science tools to provide a comprehensive business solution. Follow the STAR framework for behavioral questions and research BCG X’s culture and mission. It’s also helpful to check out the corporate BCG X website to see the challenges they are currently grappling with.

1. Can you describe your understanding of BCG X in 3–5 sentences?

This question helps interviewers gauge whether you have a clear picture of BCG X’s role within the broader BCG context.

How to Answer

Provide a concise summary of what you’ve researched on BCG X, such as its focus on digital ventures and advanced analytics. Talk about how it transforms traditional consulting practices and their context within the broader BCG landscape.

Tip: network with BCG X employees or ex-employees on LinkedIn to get a clearer picture of the firm and BCG X’s function within it. Give an example of a successful client case from BCG X’s blog.

Example

“BCG X is the tech build and design unit of Boston Consulting Group, specializing in building digital and technology-driven solutions. It combines BCG’s strategic consulting with technology to develop solutions to address critical business challenges in client organizations. An example of BCG X’s impact is its work in developing advanced analytics platforms that help retail clients optimize their supply chains.”

2. Why do you want to join BCG?

Understanding why you want to join will help your interviewer determine if your values and aspirations align with BCG’s mission.

How to Answer

Your answer should reflect your understanding of BCG’s work, culture, and the opportunities that attract you to the company. Be honest and specific about how their offerings align with your career goals.

Example

“I am eager to join BCG because of its commitment to client success and internal professional development, which is ideal for the kind of career I envision. I am drawn to BCG’s approach to integrating data science with strategic consulting, which I believe is the future of the industry.”

3. Can you describe the last analytical tool or technique you taught yourself, why you chose it, and how you taught yourself?

You need to be able to independently learn new technologies or analytical techniques, as you will work on a lot of POCs where you’ll be expected to impress clients within short time frames.

How to Answer

Emphasize how the techniques you learned align with current industry trends or specific challenges you faced in projects.

Example

“I recently began diving into the fundamentals of large language models like GPT-4. I chose to learn this as enhancing customer service was a project I recently worked on for a client. I used resources like the OpenAI documentation and online tutorials and applied what I learned by building a prototype chatbot for my former client.”

4. What is your approach to resolving conflict with co-workers or external stakeholders?

The interviewer needs to know how you handle conflicts, as diverse opinions often converge in high-stakes consulting projects.

How to Answer

Illustrate with a concise example to highlight your customer-centricity and ability to lead.

Example

“My approach to resolving conflicts involves a few key steps. First, I ensure all parties involved have a chance to express their views, which also helps me understand the root cause of the disagreement. Next, I encourage a discussion focused on finding common ground and aligning it with overarching project goals. For instance, during a previous project, there was a disagreement on the analytical approach between our team and the client. I led a meeting where we outlined the pros and cons of each approach and agreed on a hybrid method that enhanced the project outcome.”

5. Can you share a time you were a part of a highly successful team? What made it so successful?

This question seeks to uncover what role you would naturally assume and how you would contribute to the team’s success.

How to Answer

Emphasize elements like leadership, communication, diversity of skills, and effective problem-solving.

Example

“I was part of a cross-functional team developing a model to reduce churn for a telecom client. What made us work was how well our skills complemented the team and the fact that each member knew exactly what their role was. My work centered around model testing, but I also took on the responsibility of coordinating communications between data scientists and business analysts. We held regular meetings to brainstorm solutions and make sure that we weren’t losing sight of the overarching goals. We were collectively able to reduce churn by 15% over four months and also went above and beyond to improve the client’s customer service, leading to higher customer satisfaction scores.”

6. Over budget on a project is defined as when the salaries, pro-rated to the day, exceed the project budget. Write a query to forecast the budget for all projects and return a label of ”over budget” if it is over budget and ”within budget” otherwise.

This question tests your SQL skills in the context of handling practical scenarios you might encounter as a BCG X data scientist.

How to Answer

Describe the database schema you would use or expect to have for such a query. You should mention which SQL clauses you’d employ.

Example

“I would assume access to a database with tables for Projects, Employees, and ProjectAssignments. The query would join these tables to align each project with the employees working on it and their corresponding salaries. I would then calculate the total salary expense by multiplying each employee’s daily rate by the days they worked on the project. This result would then be summed for each project. Using a CASE statement, I’d compare this total expense against the project budget to classify each project as either ‘over budget’ or ‘within budget’.”

7. Explain the trade-offs between bias and variance in machine learning models.

In machine learning projects, you need to understand this crucial trade-off to reconcile your technical knowledge with your domain expertise, for example, in a customer churn prediction problem.

How to Answer Define bias and variance in the context of machine learning. Explain the trade-offs, emphasizing the impact of underfitting (high bias) and overfitting (high variance) on model performance. Discuss your recommended strategies to find the optimal balance.

Example

“Bias represents the error introduced by overly simplistic assumptions in a model. When a model exhibits high bias, it tends to oversimplify the problem and underfit the data. On the other hand, variance represents the error due to excessive model complexity, causing the model to fit the training data too closely. Techniques like cross-validation to assess model performance, regularization to control complexity, and thoughtful algorithm selection will help us evaluate the required trade-off in a specific scenario.”

8. You’re given a dataframe of standardized test scores from high schoolers from grades 9 to 12 called df_grades. Write a function in pandas called bucket_test_scores to return the cumulative percentage of students that received scores within the buckets of <50, <75, <90, <100.

Data scientists often need to categorize data to make informed decisions, and this question tests these skills in Python.

How to Answer

Walk your interviewer through your code and clearly explain the functions you’d use.

Example

*“I would start by defining the score buckets as intervals that capture the ranges of interest: below 50, 50 to less than 75, 75 to less than 90, and 90 to 100. Using the pd.cut() function, I would categorize each student’s score into these buckets. Then, I would count the number of students in each bucket and calculate the cumulative percentage of students for each category. This approach ensures that we understand not just the distribution but also how large a portion of the student population falls into each performance category.”*

9. What are the benefits of feature scaling in a logistic regression model?

This is asked to assess your understanding of data preprocessing and its impact on model accuracy and performance.

How to Answer Focus on how feature scaling aids in faster convergence during training, ensures uniformity in feature influence, and enhances the interpretability of model coefficients. Talk about the practical implications of these benefits.

Example “Feature scaling standardizes the range of independent variables, leading to faster convergence during optimization. Additionally, it allows for easier interpretation of the model’s coefficients, as each coefficient reflects the relative importance of its corresponding feature in terms of the scaled range. This is particularly useful in a retail context, where understanding the influence of different customer attributes on purchasing decisions, for instance, can help with more effective targeting and personalization strategies.”

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

Understanding risk is paramount in many businesses, especially consulting. Knowing how feature correlation affects model predictions helps data scientists assess the reliability of the model under various scenarios.

How to Answer Discuss how feature importance in random forests might be affected by correlated features and the potential misleading interpretations that might arise.

Example “Feature importance is measured using a technique called permutation importance. Permutation importance is a way of measuring the contribution of each feature to the model’s prediction accuracy by randomly permuting the values of that feature and measuring the resulting decrease in accuracy. If two features are highly correlated, they may both have high permutation importance scores because they both contribute to the model’s ability to make accurate predictions. However, how they are combined in the model can affect how they appear in a measurement of feature importance.”

11. You are tasked with developing a predictive model for a retail client aiming to optimize their marketing strategy. How would you approach building this model?

Optimizing marketing strategies is a common problem that BCG solves for its clients. Market mix modeling is also a classic problem that consultants tackle. You can practice more marketing analytics problems here and read our marketing analytics case study guide here.

How to Answer Here is our framework for tackling case-based questions:

• Define the scope of the problem through clarifying questions.
• Formulate a solution, discussing the high-level modeling techniques without getting into too many details.
• If there is an alternative solution that should be considered, touch on it.
• Mention the business outcome of your solution.
• Talk about the risks.

The interviewers will ask lots of questions, even interrupting you, while you are formulating your answer. They simply want to see if you get flustered easily and how you might handle clients who tear your solution to shreds. It’s important to keep a cool head and have your facts ready.

Example “I would first confirm the specific goals of the marketing strategy with the client. Are we focusing on increasing customer retention, enhancing customer acquisition, or optimizing the marketing spend? Based on the responses, I’d propose a model that predicts customer behaviors, such as purchase likelihood or response to different marketing channels. A supervised learning approach might be the most appropriate solution. For instance, a logistic regression model could be employed for predicting binary outcomes, like whether a customer will buy a product or not. Also, a decision tree or ensemble methods like random forests could be used to understand which features most influence customer decisions. Our solution would aim to help the client allocate their marketing budget, leading to higher conversion rates and increased ROI.”

12. Given an example paragraph string and an integer N, write a function n_frequent_words that returns the top N frequent words in the posting and the frequencies for each word.

This question evaluates your experience with text data, which is essential in projects involving natural language processing. For example, one ongoing project for BCG data scientists is improving and optimizing Casey, BCG’s chatbot.

How to Answer

Describe the process of breaking down the text into words (tokenization), preprocessing the texts (stemming, lemmatization, etc.), counting the frequency of each word, and then retrieving the top N-frequent words. Mention the Python libraries and functions you’d use.

This article provides an excellent overview of the text preprocessing steps.

Example

*“I’d first normalize the text to ensure consistency in word counting. This involves converting all text to lowercase and removing punctuation. Python’s str.lower() method can handle the case conversion, while str.translate() combined with str.maketrans() is useful for removing punctuation. Next, I would split the text into words and count their occurrences. The collections.Counter class in Python is perfect for this task as it counts hashable objects and returns a dictionary-like object. Then, I’d use the heapq.nlargest() function from Python’s heapq module, which is ideal for this purpose because it allows me to specify that I want the N largest entries based on the word frequencies.”*

13. A national restaurant chain wants to increase the average spending per customer. They have provided you with transactional data, customer feedback, and operational details from multiple locations. How would you analyze this data to identify factors that influence customer spending? What kind of predictive model would you propose to forecast the impact of potential changes?

This case study question is another typical consulting case that BCG data scientists encounter.

How to Answer

Outline a plan to analyze the given data to identify factors influencing customer spending. Discuss cleaning and integrating the data, performing exploratory data analysis, and using statistical methods to uncover correlations.

Example

“I’d first look at the data using exploratory data analysis (EDA) techniques such as correlation matrices, scatter plots, and summary statistics to identify trends and outliers.

From the transactional data, I would look at variables like time of visit, amount spent, menu items purchased, and service speed to identify factors that might affect spending. Customer feedback could provide insights into satisfaction levels, which can be linked to spending habits. Operational data might reveal the efficiency of different locations.

I would consider a regression analysis if the goal is to predict spending amounts or a classification model to categorize customers into different spending levels. Decision trees or random forest models would help us determine the factors influencing customer spending.”

14. What are the Z and t-tests?

These tests are fundamental statistical tools used to determine if there are significant differences between groups, such as in A/B testing scenarios.

How to Answer

Clearly define both tests and explain when each is appropriate to use. Highlight the differences between them, particularly in terms of sample size and population standard deviation knowledge.

Resource: You can practice more statistics and probability questions here.

Example

“The Z-test and t-test are both methods used in hypothesis testing to determine if there are significant differences between means of two groups. A Z-test is typically used when the sample size is large (usually over 30) and the population variance is known. On the other hand, a t-test is used when the sample size is smaller and the population variance is unknown. It uses an estimated standard deviation and adjusts for the uncertainty by using the t-distribution, which is more spread out than the normal distribution.”

15. How would you test whether a new marketing campaign has significantly increased sales?

This question is central to understanding your ability to apply statistical analysis to measure the effectiveness of business interventions. You’ll need to justify the impact of strategic decisions and provide evidence-based recommendations to clients to reassure them.

How to Answer

Detail the statistical tests you would use to determine the significance of changes in sales post-campaign versus pre-campaign. Also, talk about how you’d use historical data as a control if an experiment isn’t possible. Mention how you would handle potential confounders to ensure the reliability of the results.

Example

“The ideal approach would be to conduct a controlled experiment where one group of stores or regions receives the campaign while a comparable group does not.

If a controlled experiment was conducted, I’d use a difference-in-differences analysis. This method compares the changes in sales between the control group and the experimental group from before to after the campaign. It helps control for external factors that might affect sales.

If historical data must be used as a control, I would apply an ARIMA model to predict expected sales without the campaign and then compare these predictions to the actual sales during the campaign period.

In both scenarios, I’d perform hypothesis testing using a t-test or ANOVA to statistically verify whether the observed changes in sales are significant.”

16. Given that X and Y are independent random variables with normal distributions, what is the mean and variance of the distribution of 2X−Y when the corresponding distributions are X∼N(3,4) and Y∼N(1,4)?

This question gauges your understanding of basic statistics.

How to Answer

To tackle such questions, clearly state your approach before diving into the solution.

Example

“The mean of 2X −Y would be 2∗3−1=5. The variance, since X and Y are independent, would be calculated as $2^2∗4+(−1)^2∗4=16+4=20$. So, the distribution of 2X − Y is N(5,20).”

17. Consider a scenario where you’re consulting for a bank experiencing a high rate of fraud, and the data they collect often contains outliers and missing values. How would you mitigate fraudulent transactions?

You’ll frequently encounter imperfect datasets, common in industries like banking. This question assesses your ability to apply sophisticated techniques to flag suspicious activity, a typical business problem BCG solves for its clients.

How to Answer

Discuss the steps to clean and prepare the data. Then, outline the framework you would implement to assign a flag to potentially suspicious activity. Emphasize this framework and a scoring system before diving into the models you might deploy.

Example

”The process should start with rigorous data cleaning and preparation. This involves identifying and addressing outliers, which may be indicative of either fraudulent activity or errors in data collection. Techniques like IQR (Interquartile Range) or Z-scores can be employed to detect and manage outliers appropriately. Missing values should be handled carefully, using methods such as median imputation for robustness or k-nearest neighbors (KNN) imputation to preserve relationships in the data.

Once the data is clean and prepared, developing a robust framework for flagging potentially suspicious activities is next. This framework would involve creating a scoring system based on a set of rules derived from historical fraud patterns and expert input. For example, transactions that exceed certain thresholds, occur at unusual times, or deviate from a customer’s typical behavior patterns would receive higher scores. Transactions with scores exceeding a certain threshold would be flagged as potentially fraudulent.

To complement this rule-based system, I would also recommend deploying a Gradient Boosting Machine, which can learn from the data to identify subtle patterns of fraudulent behavior not easily captured by manual rules.”

18. You are given a deck of 500 cards numbered from 1 to 500. If the cards are shuffled randomly and you are asked to pick three, one at a time, what’s the probability of each subsequent card being larger than the previously drawn one?

Probability, statistics, permutations and combinations, and logical thinking are mathematical skills essential to analyzing different types of client data. Practice more probability questions here.

How to Answer Emphasize the importance of considering all possible combinations of three cards and then the favorable outcomes. Inform the interviewer what mathematical approach (binomial distribution) you are going to follow.

Example “The total number of ways to draw three cards from 500 is $^{500}C_3$. Each specific set of three cards can only be arranged in one way to meet the condition (ascending order). So, the probability is the number of sets of three cards, which is $^{500}C_3$ divided by the total number of ways to draw three cards.”

19. You are working with an oil company to help them price oil (in real time) for all their gas stations in Texas. How would you approach it? What data would you ask for?

This examines your strategic thinking and your familiarity with economic factors impacting pricing.

How to Answer Begin by outlining the types of data needed for real-time oil pricing and the analytical approach you would use. Describe how you would use this data to build a model that dynamically updates prices.

Example

“I would first request access to historical pricing, real-time sales, supply, market, and consumer behavior data.

I’d then build a predictive pricing model starting with a time series analysis to understand pricing trends and seasonality. The model would be designed to update prices in real time based on an algorithm that factors in supply-demand dynamics, competitor pricing, and market conditions.

This model could use a combination of regression analysis to forecast demand at various price levels and reinforcement learning to dynamically adjust prices based on real-time data inputs.”

20. How does random forest generate the forest? Why would we use it over other algorithms?

In BCG’s context, ensemble methods are used to address various challenges, such as demand forecasting, customer segmentation, and fraud detection.

How to Answer

Emphasize the use of bootstrapping and feature randomness. Discuss the advantages of using random forest, contextualizing it to match some of BCG’s common client problems.

Example

“Random forest generates its forest by creating multiple decision trees during the training process. Each tree is built from a random sample of the training data, taken with replacement, known as bootstrapping. Random forest introduces randomness while splitting nodes by selecting a random subset of the features to consider at each split. This randomness helps in making the model more robust against overfitting, which is common with single decision trees.

We would use a random forest over other algorithms because it reduces variance without substantially increasing bias. This makes it better for complex datasets that have a mix of numerical and categorical data, which is common in retail settings. Random forest can handle overfitting better than many algorithms, especially with high-dimensional data. It also provides feature importance scores, which can be very insightful for understanding which factors most influence the prediction target.”

Here are some tips to help you excel in your interview.

Study the Company and Role

Research recent BCG news, updates, values, and challenges the company is focused on. Understanding the company’s culture and strategic goals will allow you to better present yourself and know if they are a good fit for you.

Further, if you know data scientists at BCG X, it’s a good idea to talk to them to understand what will be expected of you.

Understand the Fundamentals

Brush up on core data science topics like statistics, machine learning algorithms, data preprocessing, and model evaluation. Be comfortable with Python, SQL, and the Python libraries commonly used for machine learning and statistical modeling, like pandas, scikit-learn, and TensorFlow. Your applied mathematics, such as statistics and probability, will also be examined. Here are additional questions on Python, SQL, probability, linear regression, and some general data science interview questions you can work on.

If you need additional guidance, we also offer our tailored data science learning path covering core topics and practical applications.

Research How Case Interviews Work

BCG X interviews focus much less on coding theory and skills than normal tech firm interviews. You’re much less likely to be asked, for example, to determine whether a sample piece of code is efficient or optimized. Rather, they focus on business problems that have advanced data analytics as key components of the solution.

You need to be able to provide a comprehensive solution to a client while justifying the impact of your work. Although knowing your stats and ML is important, that will not be the ultimate factor in getting an offer. You have to be good at explaining your solution and have a “consultant presence” about you. Visit BCG’s case interview prep guide for more information.

Prepare Behavioral Interview Answers

Soft skills such as collaboration and adaptability are paramount to succeeding in any job, especially data science consulting roles, where you’ll need to articulate and present solutions to consultants and external stakeholders.

A common framework to follow for consulting behavioral questions is the A-STAR-(E) approach:

• Answer: Provide a statement briefly answering the problem
• Star: Describe the situation at hand
• Tension: What was the conflict?
• Action: What did you do to solve it?
• Result: What was the impact of your action?
• Effect: What did you learn from this situation?

To test your current preparedness for the interview process, try a mock interview to improve your communication skills. You can also practice some more from our list of behavioral questions or project interview questions.

What is the average salary for a data science role at BCG?

The average base salary for a data scientist at BCG is $129,399, above the average base compensation of $123,030.

What other companies can I apply for besides BCG’s data scientist role?

You can apply for similar roles at other MBB companies. We have interview guides for McKinsey and Bain as well.

For insights on other data science consulting jobs, you can read more on our Company Interview Guides page.

Are there job postings for BCG data science roles on Interview Query?

Yes, we have open roles posted. You can visit our job portal; search by team, location, and current skill set; and apply for your desired role. We also list data science job openings for other firms.

Succeeding in a BCG X data science interview requires a healthy understanding of the consulting approach, fundamental statistical knowledge, and the ability to present your solutions well. You also need to keep a clear head as you will be cross-questioned in a manner similar to how a client might probe your solutions.

Understanding BCG’s focus on customer-centricity and delivering strategic value, as well as your thorough preparation with case interviews, will be vital to success. For other data-related roles at BCG, consider exploring our guides for business analysts, data engineers, data analysts, and other positions in our main BCG interview guide.

Best wishes on your journey to landing a role at BCG X!