Data science interview questions help recruiters and hiring managers assess your skills, experiences, and qualifications to see whether you’re a suitable fit for their organization. They can gauge everything from your general understanding of the profession to your proficiency in specific topics and advanced concepts.
In this article, we go through some of the most common data scientist interview questions you can encounter. We’ll explain the reasoning behind them and give examples of answers so you know what to say. Let’s dive in!
12 Data Scientist Interview Questions with Answers
There are many data scientist interview questions you can run into when applying for a job. They can vary based on many factors, including the amount of work experience you have and the company you’re applying for. For instance, data scientist interview questions for Microsoft or Amazon will likely be significantly different than those for a startup.
Here’s a list of the most frequently asked data scientist interview questions you should know how to answer to maximize your chances of impressing recruiters.
#1. What is data science?
This is one of the data science interview questions aimed at entry-level professionals. Its purpose is to assess your general understanding of the profession. Recruiters ask this to see whether you grasp the multidisciplinary nature of data science and know what it takes to succeed in the field.
Your answer should be about the essential elements of data science, such as gathering the data, cleaning it, creating models, and interpreting results.
Here’s a good example:
Good Example
“Data science is an interdisciplinary profession that combines many fields, including mathematics, statistics, programming, machine learning, and business acumen. It involves gathering and preparing data for analysis, building models, deploying features, and helping businesses develop.
One of the main goals of data science is to uncover insights and make predictions which can maximize the chances of success by ensuring data-driven decision-making.”
#2. What’s the difference between data science and data analytics?
The purpose of this data scientist interview question is to examine whether you know the difference between these two related fields. Recruiters will ask this question to see if you recognize the scope of data science and how it involves a lot more than data analysis.
When responding, you should highlight that data science requires several key data analyst skills and includes elements of analysis but also encompasses other disciplines, like machine learning or big data processing.
Let’s see that in a good example:
Good Example
“Data science and data analytics are related fields with distinct scopes and skills required. Data analytics mostly focuses on examining historical information to extract valuable insights and help drive business decisions.
On the other hand, data scientists go beyond analysis to incorporate programming, machine learning, and AI into their workflow. They leverage intricate computational techniques to build automated systems and models for high-stakes predictions.”
#3. What is the difference between supervised and unsupervised learning?
This data scientist interview question probes into your technical skills and aims to assess your knowledge of the two foundational types of machine learning. Interviewers ask this to see if you can explain the difference and if you know when to use which one.
Your answer should clearly define supervised and unsupervised learning, explaining when each is used. You should also provide an example of when you’ve used these techniques.
Here’s an example of a good answer:
Good Example
“In supervised learning, I train the algorithm on labeled data. For each input, there’s a correct output, and the goal is to predict outcomes for new information. I frequently used supervised learning for classification for spam detection.
Contrary to supervised learning, unsupervised learning works with unlabeled data, and its purpose is to identify patterns and connections. For instance, associated rule learning is typically used in Market Basket Analysis.”
#4. Explain the steps in making a decision tree. How would you create a decision tree?
When recruiters ask this data scientist interview question, they are evaluating your knowledge of one of the fundamental types of supervised machine learning. They want to gauge how well you understand decision trees and how you work with them in practice.
Your response should cover the main steps in creating a decision tree. For bonus points, you can mention specific details, such as Gini Impurity or Information Gain.
Let’s see that in a good example:
Good Example
“A decision tree is a supervised learning algorithm. It’s used for tasks that require classification and regression. The first step to creating a decision tree is selecting the best feature using criteria like Gini Impurity or Information Gain.
Following that, I would split the data into subsets based on the feature. Then, I’d grow the tree until I classify all data points or meet a stopping condition. After growing, I’d prune the tree to prevent overfitting before finally testing the model to evaluate its performance.”
#5. What is overfitting in machine learning, and how do you prevent it?
Overfitting is a common problem in machine learning associated with model generalization. Recruiters ask this question to see whether you understand this issue and know how to address it.
When answering, you should discuss various strategies used to prevent overfitting, demonstrating in-depth knowledge.
Here’s a good example:
Good Example
“Overfitting happens when an ML model learns a lot of noise and random data in addition to the basic pattern. When that happens, a model can give inaccurate predictions or underperform for new types of data.
To prevent it, we can use more training data to help with generalization. We can also apply regularization techniques, do cross-validation, or prune decision trees. Another method includes using dropouts in neural networks to prevent model dependency on specific nodes during training.”
#6. Explain the bias-variance tradeoff.
Recruiters ask this data scientist interview question to see whether you understand the basic difference between underfitting and overfitting in machine learning models.
When responding, you want to explain the relationship between a model’s complexity and prediction accuracy and discuss its prediction-making capabilities when working with previously unseen data.
Let’s see an example of a good answer:
Good Example
“The bias-variance tradeoff represents the balance between these two types of errors in machine learning models. Models with high bias are underfitted, can make overly simplistic assumptions, and often don’t catch the underlying or complex patterns.
On the other hand, models with high variance are overfitted and can make significant errors due to small changes in training data. The goal of the bias-variance tradeoff is to achieve an optimal balance with low bias and low variance to get good generalization.”
#7. What are the different types of clustering algorithms?
This data scientist interview question is designed to examine your knowledge of unsupervised learning techniques and proficiency in different clustering methods.
An optimal answer should specify several different types of clustering algorithms, explain how each works, and provide examples of how they are used in real life.
Here’s an example:
Good Example
“Clustering algorithms are used for unsupervised learning for a technique called cluster analysis or clustering. One common clustering algorithm is called k-means clustering, which is a method of vector quantization and is frequently used in customer segmentation.
Hierarchical clustering is another type that creates trees of clusters and is particularly useful in taxonomies. There’s also DBSCAN, which is great for spatial data analysis, as well as a Gaussian mixture model used in anomaly detection.”
#8. What is a confusion matrix? Explain its importance.
This data scientist interview question helps recruiters assess your ability to evaluate classification models.
When giving an answer, you should provide the definition of the confusion matrix before explaining its role in model evaluation.
Let’s see that in an example of a good answer:
Good Example
“A confusion matrix is a two-by-two table used to define the classification algorithm’s performance. It compares actual positives and negatives with predicted positives and negatives to uncover true and false positives and negatives.
This is critical as accuracy alone shouldn’t be used for evaluation. For instance, a confusion matrix can help in fraud detection to determine whether the model has too many false positives or it detects fraudulent activities correctly.”
#9. What is dimensionality reduction?
Recruiters ask about dimensionality reduction to see whether you know how to improve the efficiency of models and reduce overfitting.
Your response should highlight your understanding of specific dimensionality reduction techniques, like PCA or t-SNE. To further demonstrate your data scientist skills, you should provide real-world use cases or examples of when you employed these techniques.
Here’s what this should look like:
Good Example
“Dimensionality reduction reduces the number of features while preserving the important information to simplify the data. If not employed, it can lead to overfitting, increased computational costs, and difficulties in data visualization.
Common techniques for dimensionality reduction include PCA, which transforms features into unrelated components, and t-SNE, frequently used for visualization.”
#10. How would you handle missing data in a dataset?
Handling missing data is one of the core data scientist responsibilities and a fundamental problem-solving skill. Recruiters ask this data scientist interview question to see if you’re familiar with different imputation techniques and know how they can impact the performance of a model.
Your answer should outline the different methods for handling missing data while explaining the upsides and drawbacks of each.
Let’s see that in an example:
Good Example
“I employ several different techniques to handle missing data and not get biased models. For instance, if a feature has too many missing values, I might remove rows or columns, as it can be better than imputing.
When encountering random missing numerical values, I can opt for mean, median, or mode imputation. Forward and backward fill is great for filling missing values based on the next or previous ones.”
#11. What is feature engineering, and why is it important in machine learning?
This data scientist interview question examines your ability to transform raw data into useful features, which is vital in enhancing model accuracy.
When responding, you want to explain what feature engineering is, talk about different methods, and demonstrate how they improve the performance of a model.
Here’s an example:
Good Example
“Feature engineering is a preprocessing step for preparing raw data and transforming it into useful features. This can improve model performance and result in higher quality and more complex models.
Some of the common feature engineering techniques include binning, which converts continuous categories into groups, and one-hot coding, which is great for creating binary features. There are also scaling and normalization, text feature extraction, and more.”
#12. What is the curse of dimensionality in machine learning?
Recruiters ask this data scientist interview question to see whether you understand the challenges of working with high-dimensional data.
When responding, you should explain how the curse of dimensionality refers to overfitting and increased computational complexity. Note that it can impact machine learning algorithms but that there are techniques to overcome it.
Here’s what a good answer to this question during a data science interview should look like:
Good Example
“The curse of dimensionality refers to the deterioration of algorithm effectiveness as the amount of data increases. This can result in long training times, unnecessary complexity, and increased resource requirements.
To overcome this obstacle, we can apply dimensionality reduction techniques and leverage data preprocessing, such as normalization and missing value handling.”
15 Advanced Data Scientist Interview Questions
In addition to many common interview questions and answers you can encounter when applying for a job, there are many advanced ones that may test your knowledge and experience as a data scientist.
Here are five data scientist interview questions related to core machine learning concepts:
Core Machine Learning Interview Questions
Can you explain L1 and L2 regularization?
What are the assumptions of linear regression?
What is the difference between parametric and non-parametric models?
Explain the ROC curve and ROC AUC score.
How do you choose the right evaluation metric for a problem?
Now, here are five questions about algorithms and relevant techniques:
Algorithm and Techniques Interview Questions
What are the differences between deep learning and traditional machine learning?
How do decision trees handle categorical data?
What are bagging and boosting, and how do they differ from one another
What is the silhouette score in clustering?
What is the XGBoost algorithm, and how does it work?
Finally, here are five questions about the advanced concepts:
Advanced Concepts Interview Questions
How would you handle outliers in a dataset?
How is survival analysis used in data science?
Explain the Monte Carlo simulation and tell me where it is used.
Explain Markov chains and their applications.
Explain the difference between time series forecasting and regression
5 Pro Tips for Preparing for a Data Scientist Interview
To ensure optimal preparation for your data scientist interview, we’ve prepared several expert interview tips. These can help you form better responses and answer more confidently, and they are:
Use the STAR method. Whenever you’re presented with a behavioral question, you should answer it by using the STAR method (Situation, Task, Action, Result). This method gives you a proven structure to follow, making answering these types of questions easier while ensuring you’ve given optimal responses.
Research the company. By familiarizing yourself with the organization, its goals, products, services, values, and team, you’ll understand what they look for in candidates. This will allow you to tailor your answers to focus on the skills and experiences that are relevant to the role you’re applying for.
Leverage data science question banks. There are many free online resources where you can find hundreds more questions to prepare for an interview. These can be particularly important when looking at data science interview questions for Google, Meta, Accenture, or other large companies, as their recruiters often ask specific questions.
Review your data scientist resume and cover letter. Recruiters often refer to a candidate’s resume and cover letter when asking questions. Moreover, they’ll compare the information in them with the candidate’s answers. That’s why your answers should align with the details in these documents, especially when it comes to your skills and achievements.
Brush up on your technical skills. Recruiters might ask you specific questions about your coding skills (e.g., R or Python and its libraries), proficiency in SQL, experience with A/B testing, and so on. That’s why you should make sure to sharpen these skills before your interview.
Final Thoughts
While a data science interview is hard, a lot of the difficulty lies in not knowing what the recruiters are going to ask. Fortunately, there are some very common data scientist interview questions that, when mastered, allow you to quickly and easily get ahead of the competition.
Apart from practicing those, don’t forget to research the company, polish your technical skills, and read up on the STAR method. These are all techniques and tactics that can significantly improve your chances of leaving a lasting good impression on the recruiters. Also, don’t forget to practice with a mock interview to overcome the anxiety more easily. Best of luck!
