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 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:
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:
Job responsibilities differ by the project and client, but in general, all data scientists at Quantumblack are responsible for:
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:
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:
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.
Onsite interviews generally start with a technical screen on algorithms, machine learning, and data structures. Sample questions include:
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.
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:
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.
K-means and PCA are unsupervised learning techniques. PCA helps to reduce dimensionality before performing K-means clustering.
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.
Decision trees are a supervised learning technique that is used to categorize or make predictions based on how a previous question or set of questions were answered.
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