Data scientist interview questions

List of interview questions

1. Can you explain the concept of regularization in machine learning and why it is important?
2. How would you approach feature selection in a dataset with a large number of variables?
3. Explain the difference between supervised and unsupervised learning. Can you provide examples of when you would use each?
4. How do you handle missing data in a dataset, and what are the potential pitfalls of different imputation methods?
5. Can you explain the bias-variance trade-off and its significance in machine learning models?
6. What is the process of designing and evaluating an A/B test for a new feature on a website?

Data scientists play a crucial role in businesses. They gather, clean, organize, and analyze extensive data sets to address business challenges and derive actionable insights.

These professionals also delve into big data to identify trends and formulate hypotheses that companies leverage to make decisions about operations, target audiences, or products.

We’ve compiled a list of essential interview questions to assist you in identifying the most qualified candidate for the data scientist role and supporting your hiring process.

Interview questions for data scientist

Here are the six most important interview questions you should ask for a data scientist candidate:

1. Can you explain the concept of regularization in machine learning and why it is important?

Why ask this question

This question assesses the candidates’ knowledge of model complexity and their ability to strike a balance between fitting the training data well and generalizing it to new, unseen data.

A strong data scientist candidate should be able to define regularization, discuss its types (e.g., L1, L2), and explain how it helps prevent overfitting.

2. How would you approach feature selection in a dataset with a large number of variables?

Why ask this question

Dealing with high-dimensional data is common in data science. This question evaluates the candidates’ ability to navigate and process large datasets efficiently.

It also tests their understanding of the importance of selecting relevant features for model performance and interpretability.

A good answer involves discussing methods like recursive feature elimination, regularization techniques, or dimensionality reduction approaches.

3. Explain the difference between supervised and unsupervised learning. Can you provide examples of when you would use each?

Why ask this question

This question assesses the fundamental understanding of different learning paradigms. A data scientist needs to know when to apply supervised or unsupervised learning based on the nature of the problem.

A strong response should clearly define both supervised and unsupervised learning and provide practical examples for each. 

4. How do you handle missing data in a dataset, and what are the potential pitfalls of different imputation methods?

Why ask this question

This question evaluates the candidate’s knowledge of data preprocessing techniques and their awareness of the potential biases introduced by different imputation methods.

A good candidate should be able to address the importance of understanding the mechanism behind missing data and its potential impacts on downstream analyses.

5. Can you explain the bias-variance trade-off and its significance in machine learning models?

Why ask this question

This data scientist interview question assesses the candidates’ grasp of model performance concepts and their ability to tune models appropriately.

A strong response should define bias and variance, explain the trade-off between the two, and discuss how it relates to model underfitting and overfitting. 

6. What is the process of designing and evaluating an A/B test for a new website feature?

Why ask this question

This question evaluates the candidate’s ability to design experiments and draw meaningful conclusions from experimental results.

A/B testing is a common practice in data-driven decision-making. 

Therefore, an effective response should cover the key steps in designing an A/B test. It should include formulating hypotheses, selecting appropriate metrics, randomization, and addressing potential biases.

Furthermore, when hiring a professional for this role, you may use or customize this data scientist job description template for your job postings.