Data Scientist Interview Questions For 2024 (With 10+ Job-Winning Answers)

Looking to ace the Data Scientist interview questions for the role you are applying for?

Then you’ll need to start gearing up!

Some questions addressed during interviews are universal for any role. They’re usually questions like:

However, if you are seeking where to find recent interview questions for a Data Scientist position, look no further than this article. We’ve wrapped up the most common, role-specific questions that you’ll need for your Data Scientist interview.

In this guide, you will learn:

What Companies Are Looking For When Hiring a Data Scientist
The Anatomy Of A Job-Winning Data Scientist Interview Answer
10+ Data Scientist Interview Questions And Answers
How To Turn This Data Scientist Interview Into An Offer

Let’s dive in!

What Companies Are Looking For When Hiring A Data Scientist

What do companies look for when they're hiring a Data Scientist?

When hiring a Data Scientist, companies look for candidates with strong technical skills in programming languages like Python or R and problem-solving skills who can obtain actionable insights from complete databases.

The most required skills companies look for in a Data Scientist are:

Programming Knowledge: Proficiency in programming languages like Python, R, and SQL.
Data Wrangling: The ability to clean and process data effectively.
Machine Learning: Understanding machine learning algorithms like regressions, classification, and clustering.
Deep Learning: Knowledge of deep learning algorithms, such as neural networks.
Data Visualization: Skills in data visualization tools like Matplotlib, Seaborn, and Tableau.
Big Data Technologies: Familiarity with big data platforms like Hadoop and Spark.
Problem-Solving And Critical Thinking: The ability to approach complex data challenges with innovative solutions and a critical mindset.
Communication Skills: The ability to translate technical findings into actionable insights for non-technical stakeholders.
Domain Knowledge: Understanding of the business sector, which contributes to making data-driven decisions that align with the company's goals.
Collaboration and Teamwork: The ability to work well in teams, including non-technical members.

If you are walking into an interview for a Data Scientist role, you’ll need to be prepared to answer these questions in a way that shows you have mastered the most important skills required for the position. This goes way beyond simple, one-sentence answers. Let’s break it down in the next section.

The Anatomy Of A Job-Winning Data Scientist Interview Answer

Remember this: every question you’re asked during an interview rarely asks for a basic answer.

For instance, let’s say the hiring manager asks you what motivates you.

Sure, you could answer something like “What motivates me is working in cross-functional roles”.

Or…

You could go with something like this:

“What motivates me is the opportunity to work cross-functionally with teams to meet client expectations. For instance, two years ago, a client signed on to pilot a $10M deal. We didn’t find out that they were upgrading the websites for their entire brand portfolio until after the deal was done.

Their current platform didn’t allow us to capture data that was crucial to our value prop (90% of what we sold them). At our 3 month check-in, the client told us we had to deliver useful data in the next month or they would take their business elsewhere and the $10M deal would be done.

Since the data wasn’t readily available we needed an alternative. I set up a cross-functional meeting with our engineering team and the client agency (who had site data in 3rd party tools). While our team extracted the data, I created a team of three analysts and briefed them on our needs. They dug into the agency’s 3rd party data and manually matched it with data in our system.

We were able to get the data we needed for our client. We met with the client to present our solution. We showed them how we matched the data and walked them through the full breakdown of how these granular insights could improve their ad-targeting data to reduce costs and drive more sales.

That meeting resulted in a contract extension worth 316% ($20M+) more than the original deal.

Additionally, our team standardized a process for data matching so we could avoid this situation in the future.”

Much better, right?!

If you find this approach a bit daunting and you’re getting chills just by thinking about how you’ll craft a thorough answer, don’t worry, because I have news for you.

The truth of the matter is this answer actually follows a secret framework many candidates are unaware of. It’s called…

The 4-Part Framework of a Job-Winning Interview Answer

The example above follows a specific framework that breaks down into four parts:

Part #1: Tie Directly to The Role

This part doesn’t actually go into the answer, but it is the first action you should take when preparing for interviews. Before you begin crafting your answer, make sure that the example and story chosen tie back to the specific qualifications and goals required for the role.

This exercise will help:

Find 10+ job descriptions that align with your target role
Review each job description for skills and qualifications
Add each new skill to a spreadsheet. For each repeat skill, add a +1 in the column next to the skill
Sort from Highest to Lowest in the frequency column

Use the top 5-10 skills as the basis for the examples and answers you choose to use in this process

Also, some recruiters might open your resume during the interview, so you'll want to make sure your resume matches the job description. Head over to ResyMatch.io to easily scan your resume and see how it matches the job description. Or, you can simply upload your resume and copy and paste the job description below, for free!

Part #2: Set The Stakes

Then, begin your answer by setting the stage and presenting the problem, issue, or opportunity you faced in the given situation. Outline the stakes, such as the wins and losses you would probably face with a positive or negative outcome, then hook your listeners with a cliffhanger.

Part #3: Show The Work

Walk the interviewer through your problem-solving process step-by-step and then include specific examples of skills and experiences the role requires.

Part #4 Wrap Up With A Win

Finish off by highlighting the wins that came from your work with measurable results!

Let’s analyze the example above:

How to Use the 4-Part Framework to Answer Data Scientist Interview Questions

By using this technique, you’ll be able to answer most of the Data Scientist interview questions, like:

How you balance data-driven decision-making with other considerations (e.g., business needs): Be sure to address a situation where you had to balance your actionable insights with other considerations and explain your approach. Explain what the stakes were and how the company would benefit from your work. Then, walk through the steps you took to approach this challenge and the ultimate results you and your team achieved.

How you work with tight timelines: State a situation where you had to work under a tight deadline and what was at stake. Explain why you had to go down the path you took, go through the challenges you faced during this process, and walk through the results you achieved.

See?

This 4-part framework can be easily applied to nearly any question. And, by applying this technique, you’ll be able to show your value and prove you are the right fit for the role!

10+ Data Scientist Interview Questions And Answers

Now that we’ve covered the anatomy of the job-winning interview answer, it’s time you learn what questions might pop up during the interview.

You may expect technical Data Scientist interview questions and also more general behavioral questions. Let’s break them down below!

Data Scientist Technical Interview Questions

Most technical questions will be aimed at understanding if you are fit for the challenges of the role given specific aspects of the company and the team you will be working with.

While they might vary depending on the role you are applying for, here are a few examples of Data Scientist technical interview questions, with answers elaborated using the 4-part framework.

Question #1: How would you handle missing or corrupted data in a dataset?

This question tests your ability to pre-process data before analysis or model training.

Here’s how you could craft an answer:

Example Answer For Questions About Missing Data

“In a project at InnovateX in 2020, we were tasked with predicting customer churn rates but discovered 20% of the dataset was missing crucial customer interaction data. Accurate predictions could save the company millions by retaining customers.

Facing the risk of skewed analyses, we devised a plan to tackle this challenge. Initially, we conducted a comprehensive data audit to identify patterns in the missing data. Utilizing domain knowledge, we imputed missing values for continuous variables with the median and employed mode imputation for categorical variables. For corrupted entries, a rule-based cleaning algorithm was developed based on data integrity constraints.

This approach improved the dataset's completeness by 95%, enhancing our model's accuracy. The result was a 30% reduction in churn rates within the first quarter post-implementation, and our predictive model's accuracy increased by 15%, showcasing the direct impact of clean data on business outcomes.“

Question #2: Can you share details about a machine learning model you've worked on? Explain the goal and how you improved its performance.

This question allows you to showcase your hands-on experience with machine learning projects.

Here’s how you could craft an answer:

Example Answer For Questions About Machine Learning Experience:

“While at DataSolutions in 2019, I developed a recommendation engine to personalize content for users on our platform. The goal was to increase user engagement by tailoring content suggestions. Initially, the engine was based on a collaborative filtering algorithm, which showed promise but had limitations in scalability and novelty.

To overcome these challenges, I integrated a content-based filtering approach, enhancing the model with user preferences and item features. Specifically, I used natural language processing to analyze item descriptions, creating a richer feature set for recommendation.

This hybrid model approach significantly improved recommendation relevance, evidenced by a 25% increase in user click-through rates and a 40% rise in user session time. The success of this project not only boosted user satisfaction but also increased our platform's ad revenue.“

Question #3: How do you ensure your model is not overfitting?

This question assesses your knowledge of model evaluation techniques.

Here's how you could craft an answer:

Example Answer For Questions About Overfitting

“In a project at FinTechPlus, tasked with predicting loan defaults, the initial model exhibited high accuracy on training data but performed poorly on unseen data, indicating overfitting.

To address this, I employed several strategies. Firstly, I introduced cross-validation, using the data to train the model in multiple folds to ensure its performance was consistent across different subsets. Additionally, I simplified the model by reducing the complexity of the decision trees used, trimming down to the most relevant features. I also applied regularization techniques to penalize overly complex models.

These adjustments led to a more generalizable model, reducing the variance between training and test accuracy from 20% to just 5%. Consequently, the model's prediction accuracy on unseen data improved by 18%, significantly reducing financial risk for the company.“

Question #4: Can you explain the concept of a p-value in hypothesis testing?

A question like this checks your understanding of statistical concepts that are crucial in data analysis and interpretation of model outputs.

Here's how you could craft an answer:

Example Answer For Questions About P-Value

“During a project at HealthTech Innovations, we aimed to determine if a new drug was more effective than the current standard treatment. The p-value became our critical statistic for this decision.

I explained to the team that the p-value measures the probability of observing our experimental data, or something more extreme, if the null hypothesis of no difference in treatments were true. For this project, a low p-value (below 0.05) would indicate a statistically significant difference in drug efficacy.

By conducting a well-designed clinical trial and analyzing the data, we obtained a p-value of 0.03, suggesting strong evidence against the null hypothesis. This result led to two wins: firstly, the validation of the new drug's effectiveness, and secondly, the potential for improved patient outcomes, underlining the importance of statistical rigor in healthcare innovations.“

Question #5: What steps would you take in making a decision tree?

This question allows you to showcase your problem-solving and decision-making abilities.

Here's how you could craft an answer:

Example Answer For Questions About Decision Tree

“At RetailX, during the optimization of our inventory management system, we used a decision tree to predict product demand. Our goal was improving inventory efficiency and reducing waste.

I started by selecting relevant features, including historical sales data, seasonality, and promotional activities. Using the Gini index, I determined the best splits to maximize the purity of the nodes. Pruning techniques were applied to avoid overfitting and ensure the model's generalizability.

This methodical approach led to a 30% reduction in overstock and a 20% decrease in stockouts, optimizing our inventory levels and improving profitability.“

Data Scientist Behavioral Interview Questions

Some questions won’t address specific skill sets, but rather behavioral aspects. Here are a few examples you might come across during your interview: