How to Prepare for Data Science Interviews (Updated for 2024)

Congratulations on being among the top 8.75% of candidates to secure a data science interview. While the primary interview certainly brings you closer to landing the data science job, it’s only the first step in what you may consider a rigorous chain of technical and behavioral interview rounds.

Since you’ve come across this article, you likely have plenty of questions about how to approach the interview and best prepare to boost your chances of landing the job. We’ve worked closely with our data science candidates to address these and other common questions—creating this comprehensive guide for you. Let’s dive in!

If you’d prefer to watch a video, see this overview of data science interview prep, which covers the four steps in detail:

The 2024 data science job market reports are available on the Interview Query blog. As we noted in November, the trends indicate a recovering economy and a stabilizing job market.

The October 2024 job market shows a strong 78.14% increase in data science roles, signaling recovery after recent slowdowns. While data analyst openings rose by 2.07%, positions for data scientists, machine learning engineers, and data engineers saw declines. Meanwhile, emerging roles like data science engineer and AI scientist saw significant jumps of 425% and 137%, respectively.

Layoffs in October 2024 were at their lowest for the year, continuing a downward trend after peaks in early 2024. However, FAANG’s share of total job openings dipped slightly by 4.8% this month.

Depending on the job description and your potential responsibilities, the intensity of your interviews may vary. To thoroughly prepare for a data science interview, gaining an in-depth understanding of the specific role and the company’s unique interview format is critical. Here’s how to approach each aspect:

Analyze the Job Description

Begin by thoroughly reviewing the job listing to identify core responsibilities and required skills. Focus on specific technical skills that are emphasized, such as SQL, Python, machine learning algorithms, or domain knowledge (finance, healthcare, etc.). Highlight whether the role is more research-focused, based on advanced models, or practical, which often involves stakeholder communication.

Research Company Projects

Look up recent data science projects, publications, or case studies shared by the company to understand how they apply their resources and available skills. For instance, a retail company may use data science for customer segmentation and recommendation systems. In contrast, a tech company may focus on machine learning for AI advancements to improve QA handling.

Identify Key Competencies

Researching the company and its projects should give you an idea about what key competencies they value the most. This could include specific programming languages, machine learning frameworks, statistical analysis, or even soft skills like communication and teamwork. This information will help you prioritize areas to highlight in your responses and tailor your technical preparation.

Survey the Competitors

Researching companies that compete directly with the one you’re interviewing for allows you to learn about industry trends, essential tools, and potential competitive skills. For instance, if competitors are advancing in predictive modeling, highlight relevant projects you’ve done.

If you’re interviewing for a role at a major tech company, consider similar organizations that work in the same domain, whether it’s AI research, e-commerce, or analytics.

Once you understand what competitors are doing, it’s easier to grasp what makes the company unique. Use this information to tailor your answers, showing you know how the company stands out and how your skills align with its particular approach.

Ask the Recruiter and Research Your Interviewer

During preliminary calls, ask the recruiter about the role’s features and the interview process. For instance, you could ask which skills or experiences are especially valued for the role, which projects or initiatives the team is currently focused on, and if a particular interview round is typically challenging for candidates.

Familiarize yourself with the typical stages of data science interviews at the company. These usually include an initial phone screen, technical assessments (coding, SQL, or data challenges), case studies, and final behavioral interviews. Understanding the typical sequence allows you to prepare progressively.

While researching, look for shared experiences, interests, or connections. This can help you build rapport during the interview. Mentioning relevant topics (such as an area of specialization) shows your genuine interest in the company and the conversation itself.

Read Interview Experiences

Our website contains interview reviews from candidates at various companies. These can provide insight into specific interview questions, common challenges, and the overall format for each stage. Understanding common interview patterns at the company helps you anticipate questions and topics.

When preparing for a data science interview, a solid review of your core competencies and technical skills is essential. Data science roles are highly specialized, and interviewers will expect you to demonstrate strong foundational knowledge and practical expertise in various technical areas.

Focus on your familiarity with those question topics. Do this by creating a list of questions that could be asked in the interviews and ordering them by the most important.

For example, if you’re studying for the Google data scientist interview, prioritize statistics and A/B testing and algorithms, and then work on machine learning and SQL questions, with some probability questions to round it all out.

Practice a few different levels of statistics interview questions to gauge your knowledge. If you find yourself effortlessly solving the easy and medium questions, then you can confidently advance to another subject where the questions are a little harder to grasp.

The goal is to have a list of question topics matched to your confidence level. This way, you’ll have an idea of which subjects you need to spend the most time practicing.

• A/B Testing: High Competency
• Statistics: Medium Competency
• SQL: Low Competency
• Algorithms: Medium Competency

Moreover, employers often focus on case studies during interviews, where you’ll be asked to analyze a hypothetical business scenario and propose a data-driven solution. You’ll be expected to break down complex problems, ask questions, and outline your approach.

Data science roles also often require you to interpret analytical results and make recommendations based on data insights. Practice explaining why your analysis or model is meaningful and how it can lead to actionable results for the business.

James Clear once said: “In the gym, if you experience no stimulus, your muscles won’t grow. If you step under 10,000 pounds, your body will break.”

Studying for data science interviews is very similar.

We are in the business of building brain muscles to solve very specific problems. Consistently practicing bite-sized interview questions allows you to level up slowly and build on your existing knowledge. You can only solve medium-level questions once you’ve aced easy questions, and so on for hard questions.

Additionally, we’ve learned at Interview Query that the more problems you study, the more likely you’ll encounter familiar questions and crack the interview. We’ve done a study on that, too.

We looked at exit survey data from Interview Query members and saw a correlation between goal achievement and the number of questions studied:

So, building an interview study habit is easier than it sounds. My advice is to try two specific things:

1. Make solving questions easy

Most of the time, it involves taking small steps. Start by solving some easy interview questions or just trying a few multiple-choice questions. Perhaps begin by just thinking about a problem for 5 minutes.

The goal is to build a habit, and the best way is to make the first steps fairly easy. Then, as you get used to the routine, you can steadily increase the difficulty.

2. Give your inbox a nudge

Studies show that nudges toward your goals are great resources for building positive habits. Similar to companies like Facebook, which send you notifications to get you addicted to Instagram, you can also do that by studying to help you achieve your goals!

Further, you can follow these key points to build a structured study and practice plan:

• Define Your Timeline: Set a clear timeline based on the interview date, allowing for regular study sessions and mock interviews.
• Identify Core Areas: Outline the essential topics to cover, such as statistics, probability, programming (Python, R, SQL), machine learning, data wrangling, and data visualization.
• Break Down Topics: Divide each core area into subtopics (e.g., regression, classification, clustering for machine learning) to tackle systematically.
• Set Daily and Weekly Goals: Establish specific goals for each study session, focusing on one area per session to ensure depth.
• Practice Coding and Algorithms Regularly: Allocate time for practicing coding questions on our platform, focusing on SQL and Python exercises relevant to data science.
• Review and Reinforce Concepts: Regularly review statistical concepts, machine learning algorithms, and data manipulation techniques to reinforce understanding.
• Implement Mini-Projects: Practice end-to-end data projects that apply key concepts, helping reinforce skills in a practical context.
• Simulate Case Studies: Work on case studies and hypothetical scenarios to practice structuring and analyzing real-world data problems.

It’s hard to grind and study for data science interviews without getting feedback. Continual feedback makes tasks much easier because we receive recognition or insights into our work to keep it from feeling like a slog.

Mock interviews and coaching are great simulations for testing your performance with another person, but AI Interviews work better in 2024.

If your mock interview partner is a data science coach, they can dive into your processes and explain exactly how you can improve in certain areas and further develop the skills to effectively communicate your ideas and solutions.

Good luck studying for the data science interview. If you need some help, Interview Query offers a variety of resources, including:

• 500+ Real Data Science Interview Questions
• Data Science Interview Course
• Facebook Data Science Interview Questions guide