Top 15 Quantumblack Data Science Interview + Guide in 2024

Quantumblack is a UK-based data science consultancy that is part of McKinsey & Company. Quantumblack provides expertise at the “intersection of strategy, technology, and design,” and prides itself on being a provider of advanced data science beyond optimization to business problems.

Data scientists at Quantumblack work on a variety of advanced data science projects across a range of industries, including healthcare, energy, sports, and travel. Quantumblack interviews are rigorous. They do test candidates on typical subjects like algorithms, machine learning, and Python, but, unique to QB, the case study and behavioral questions asked in the Personal Experience Interview (PEI) are also an important part of the interview process.

This guide to the Quantumblack data science interview offers tips and advice, sample Quantumblack data science interview questions, an overview of the interview process, and a look at Quantumblack data science roles.

Data scientists at Quantumblack work on multi-disciplinary teams of data engineers, ML engineers, designers, and project managers. Data scientists in particular are tasked with “harnessing data to provide real-world impact for organizations.”

As a consulting firm, your team and the solutions you develop as a data scientist at Quantumblack evolve with the clients’ needs. Therefore, you’ll have the chance to work on a variety of data science projects across industries, including healthcare, automotive, energy, and elite sport. Some examples of QB work include:

• Improving clinical trials - Using data science techniques to optimize site selection for clinical trials, as well as creating predictive models for enrollment periods.
• Athlete health monitoring - Using a range of performance data to predict athlete injuries.
• Oil field optimization - Using advanced analytics to minimize loss production opportunities through optimal operational decisions.
• Reducing travel delays - Optimizing baggage allocation to decrease carousel waiting times and improve travelers’ experiences.
• Positioning models for Formula1 - Combining telemetry data to build a positioning model for a racing line.

Data scientists at Quantumblack must possess a wide range of skills. Collaboration and communication top the list, as the firm has a multidisciplinary culture. At Quantumblack, you’ll collaborate with a wide range of stakeholders, from colleagues to clients, to develop custom DS solutions.

In terms of technical skills, they can change by the project. But Quantumblack works primarily with these technologies:

• Python
• PySpark
• TensorFlow
• PyTorch
• SQL
• Airflow
• Databricks
• Kedro (an in-house OSS)
• Docker & Kubernetes
• AWS, GCP, and Azure

Job Responsibilities

Job responsibilities differ by the project and client, but in general, all data scientists at Quantumblack are responsible for:

• Working with complex and extremely varied data sets to solve real-world problems
• Developing data science products and solutions for clients as well as for the QB data science team
• Writing highly optimized code to advance QB’s internal Data Science Toolbox
• Collaborating with the data engineering team on modeling
• Taking part in research & development
• Work on one of the most advanced data science teams globally

The top three skills in QB data science interviews are machine learning, algorithms, and Python, which are tested at each stage of the interview process.

Quantumblack, McKinsey’s advanced analytics firm, is known for its rigorous data science interview process. The process starts with an adaptive two-hour coding assessment, before moving into technical interviews, a case study interview, and, in some cases, a technical presentation.

In general, Quantumblack data science interviews include:

Initial Coding Assessment

This is a rigorous two-hour test that covers R, Python, statistics, and data modeling. Questions tend to be a mix of theory and practical coding. Sample questions include:

• Python data science interview questions
• Python machine learning questions
• Data modeling case studies
• Probability and statistics practice problems

HR Screen

This will entail a brief call with a recruiter to learn more about the role, and determine if you’re the right cultural fit for the position. Expect behavioral questions related to collaboration and communication.

Technical Screen

Onsite interviews generally start with a technical screen on algorithms, machine learning, and data structures. Sample questions include:

1. What are the key differences between classification models and regression models?

2. What are the assumptions of linear regression?

3. Explain boosting step-by-step. How does an XGBoost model work?

4. What is the difference between XGboost and random forest?

5. Suppose you have a binary classification model for loan rejection. How would you provide an applicant with a reason for their rejection?

6. Would a linear regression or random forest regression perform better for predicting booking prices on Airbnb?

Case Study Interview

A classic data science case interview that will ask you to investigate a business problem. Quantumblack case interviews typically focus on modeling and machine learning cases, although product and business cases may be included.

A Personal Experience Interview

Common in Quantumblack and McKinsey Data Science interviews, the PEI is a behavioral interview, covering your problem-solving approach, leadership style, entrepreneurial drive, and the impact you’ve had on your work. Sample questions include:

7. Explain a challenging situation you encountered when working with someone with an opposing opinion.

8. Talk about a time you worked to achieve something that was outside your comfort zone.

9. Discuss a hypothetical client scenario to help us understand how you structure tough, ambiguous challenges, identify important issues, deal with the implications of facts and data, formulate conclusions and recommendations, and articulate your thoughts.

10. Share an example of a time you effectively worked with people with different backgrounds.

Technical Expertise Interview

The TEI is a deep dive into your area of expertise. Usually, these interviews ask detailed questions about your technical skills, theory-based questions, and more.

11. What’s the relationship between PCA and K-means clustering?

K-means and PCA are unsupervised learning techniques. PCA helps to reduce dimensionality before performing K-means clustering.

12. You flip a fair coin 576 times. Without using a calculator, calculate the probability of flipping at least 312 heads.

Hint: What sort of probability distribution should we use to model experiments with only two outcomes? Remember that we’re modeling only the probability that we flip heads at least 312 times, not tails.

13. What is a decision tree?

Decision trees are a supervised learning technique used to categorize or make predictions based on how a previous question or set of questions was answered.

14. In which case would you use a bagging algorithm versus a boosting algorithm? Give an example of the tradeoffs between the two.

Bagging and boosting are ensemble learning methods, where we train multiple estimators to combine to form a single model with superior performance. The main difference between bagging and boosting algorithms is that bagging estimators are independent while boosting estimators are dependent.

15. Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

When a straight line or hyperplane can perfectly separate data, the model can struggle to find an optimal solution because the likelihood function keeps increasing without reaching a peak. This issue, known as a failure to converge, can be resolved by introducing regularization, which adds a penalty for overly large coefficients, ensuring the model finds a balance and converges effectively.

Additional Quantumblack Interview Questions: