9 Data Science Project Interview Questions You Should Know

Project-based behavioral questions are some of the most common inquiries you will receive during a data science interview. These questions, at the most basic level, ask you for a high-level overview of a data science project and how you worked towards the final solution.

For example, you might be asked: “What was your best data science project”? Or, “Tell me about a data science project you worked on that was a success”?

Your goal with these project-based behavioral questions is to demonstrate your technical skills, depth of domain knowledge, problem-solving abilities, and communication capacity. Taking a wide-angle look, there are three main types of data science project questions to prepare for:

• Resume questions - These questions look at your resume and experiences. An example would be, “tell me more about X or Y on your resume”.
• General data science project questions - These questions ask you to provide an overview of a data science project. An example would be, “tell me about your most successful data science project you have led or contributed to”.
• Scenario-based data science project questions - This type of question asks about projects but is more narrow in scope. An example question would be, “tell me about a project you worked on that informed a business decision”.

For most other data science behavioral questions, using a simple framework like STAR is the best way to structure your answer. Unlike other behavioral questions, however, when you are asked about a data science project, you want to be succinct in your answers and move the conversation to the challenges, solutions, and results from a portion of your project.

Here is a four-step framework to guide your discussion of past data science projects within an interview:

• State the goal - Clearly state the project’s goal and tie it to a specific business outcome. Spend just a few sentences providing this overview. Again you want to be brief here.
• Describe the solution - Explain what challenges you faced while tackling your goal and the solution you ultimately proposed. Add 1-2 technical and/or business-driven challenges you faced, including issues like gaining buy-in or managing large-scale data.
  
• Results - Describe the outcome, and if possible, be data-driven in your response. Don’t say, “the project increased revenue”. Instead, put forward that “we grew revenue by 15% after implementing my model”. Pull two or three interesting insights you found when you talk about the results.
• Lessons learned - Finally, tell the interviewer what you learned by working on this project. You could mention a new skill you picked up or how you developed a better process for gathering stakeholder requests. This shows that you are transferring knowledge and processes to improve your future contributions to a team or project goals.

Ultimately, how you answer a data science project question comes to the audience. In interviews, you will likely have three main audiences, and you should alter your responses for each of these distinct audience types:

• Non-technical stakeholders - Stay high-level in your project description. You can lose the audience if you get too far into the technical details. One rule to follow: Focus on the business outcome of your project.

• Data science managers - You can get into the technical details with managers. Still, they want to know that you can also contribute qualitatively. Therefore, your responses should focus on results, creative problem-solving, and communication, with technical skills sprinkled throughout.

• Data scientists - With this audience, your responses should focus on the technical challenges, proposed solutions, and how you worked with colleagues to achieve your solution. The results do matter, but this is a great opportunity to detail the depth of your technical skills with potential peers who also have similar depths of knowledge.

Inflating your resume might land you an interview, but be prepared to back up your claims. Resume-based data science project questions will look at specific claims in your resume and ask for more details.

For example, suppose you noted that you worked on a project that increased monthly revenue by 30%. In that case, you should expect questions about that project. Resume questions might also be more generic, asking how your previous work experiences align with the position you are interviewing for.

1. What projects did you work on in your previous position that will prepare you for this role?

With this question, the interviewer is trying to assess your background, learn about your technical skills, and understand if you would make a good fit for the position. Here is a sample response:

“At my previous job, I worked on customer feedback analysis. My job was to help the product development team understand who their customers were and how the product fit their needs, with the goal of helping to improve customer UX and grow our retention rates.”

“For example, one project I worked on was UX improvements based on customer analytics collection. I gathered data about our customers, their feedback, and developed a report on our demographics and segments. Using this report, we were able to tailor the UX to match specific demographic needs, which led to a 10% lift in retention on a month-to-month basis. The deep understanding of customer analytics I gained would help me hit the ground running in this position”.

2. What tools did you use most frequently in your previous job?

A question like this assesses your technical skills. It serves as an opportunity for you to demonstrate mastery of a particular tool and that you have researched the open position’s requirements. Provide specific examples of the projects/work you executed with a specific tool. You could say:

“I know from my conversations with the recruiter that reporting and presentation will be a key job function in this role. When I heard those requirements I knew I would excel, because of my deep experience in Tableau development.”

“In a previous role, I was responsible for building visualizations and dashboards for the executive team. For that project, I gathered feedback and wrote SQL queries to pull complex metrics. Using those reports, I developed the pipeline to migrate that data into Tableau to be available in real-time to stakeholders, while communicating that I was available to explain certain results or iterate on new business cases”.

3. Your resume says you were able to achieve X results. How did you do that?

Using data and statistics can help your resume stand out, but remember that it will draw the scrutiny of hiring managers. If you say a project increased monthly revenue by 30% for an Ecommerce company - but that increase occurred between November and December before subsiding outside the holiday season - your claim will not stand up to scrutiny.

Here are a few tips for setting yourself up for success on result questions:

• Choose attributable successes for your resume.
• Use statistics to highlight the impact of your work (e.g., 10% revenue growth, 15% decline in churn).
• For the statistics that you use, prepare answers in advance for talking about those projects.

For example, you might say, “Yes, that is correct, my product recommendation engine project did help increase conversions by 10%. I was with an ecommerce company, and we were using a Bayes recommender for products. I helped develop a system that was more personalized to customer attributes, and as a result, we saw a lift of 10% after validating with an A/B test”.

4. Tell me about your favorite / most successful data science project.

Be prepared to talk about three to four projects that are most relevant to the role. In terms of success, you might ask a clarifying question or provide a definition. As you move to answer the question, you may respond as, “I’ve had several successful projects, but one that stands out and that generated the most business value was X”.

Here is a sample approach to this type of question:

• Goals. “In my previous role as a data scientist at a fintech company, I led a project to improve our ML model to predict fraudulent transactions”.
• Solution. “As I was developing the model, I faced one key technical challenge, which was that the dataset was very unbalanced. There are far fewer fraudulent transactions compared to safe transactions. I tested a variety of techniques to handle the imbalances, including synthesizing new instances of the minority class using SMOTE and using balanced bagging classifiers”.
• Results. “After A/B testing the models, we found that the new model had greater precision and improved fraud detection by 15%. It has helped prevent an estimated $X in losses”.
  
• Lessons Learned. “This project provided hands-on training for dealing with imbalanced datasets, as I was able to test a variety of techniques. I am now more confident handling these types of datasets”.

5. What was the biggest challenge you had to overcome in a data science project?

This question is common in behavioral interviews, and you can revert to a framework like STAR to explain the project’s Situation, Task, Actions, and Results for the project. Here is a sample response:

• Situation - “In a previous job, the dataset we were using had issues with missing data”.
• Task - “My job was to determine if we could limit deletion and create a dataset large enough for making accurate predictions”.
• Action - “I explored imputation and deletion strategies, to ensure we weren’t biasing the estimate”.
• Results - “Ultimately, I was able to minimize deletion, while helping to create a clean unbiased estimate”.

6. I see you’ve done X project before. What were some of the mistakes you made?

This question is designed to see how you handle adversity and what steps you take to overcome challenges. A response might look something like this:

“In my last position at an Ecommerce company, I was given a goal of developing a system to predict which customers were most likely to churn, which the company could then use to send personalized promotions to preempt their decision to leave our services”.

“The goal was to increase customer retention by 20% and revenue by 10% over one year. It was a big project, and I tried to do all of it by myself, rather than working with my team of engineers and analysts.”

“As a result, we missed our retention goal. After reviewing what went wrong in my approach, and consulting with team members, I internalized the fact that you have to delegate tasks and collaborate. I was able to make some changes over the next quarter and build upon my leadership skills. Since then, we’ve been able to consistently maintain project deadlines and increase our output”.

These questions propose various scenarios and ask you to select a project you worked on that aligns with that scenario. For example, the interviewer might say, “One of our key goals is to increase customer retention. Have you worked on any projects that helped improve customer retention”?

7. Tell me about a project you worked on that informed a business decision.

With this question, the interviewer wants to know that you can generate business value with data science. Focus on the results and how the project’s goals influenced your choices. An example response:

“Previously, I worked for a swiping job application company, and the company’s revenue was earned on a per application basis. So our goal was to increase the number of applications submitted”.

“The baseline system was a naive Bayes recommender that relied on manual tagging. I designed an alternative model, an elastic search, that I felt would provide better results and eliminate the need for manual tagging. Ultimately, the new model resulted in a 10% lift in applications, which we validated with an A/B test, while also eliminating the manual portion of the process”.

8. Tell me about a time that you had to perform X.

In these types of project questions, the interviewer wants to see specific examples of the type of work that you have performed. X could be any number of things like “building a recommendation system” or “had to clean and organize a large-scale dataset”. Your response should be structured like this:

“I have a lot of experience wrangling large-scale data. In a specific instance, I had to gather insights on a large-scale real estate dataset for a housing price forecasting model. After scraping 100,000 listings available online, we found that more than 20% of the listings were missing square footage data. I tested several approaches to dealing with this missing data, including modeling different training datasets, but found that imputation was the most reliable solution”.

See a solution to a missing housing data modeling question.

9. Tell me about a project you worked on, in which you disagreed with the project’s direction.

This type of question is vague and broad. However, it provides a chance to highlight soft skills like communication and collaboration and your depth of data science knowledge. Although the focus is on your conflict resolution approach, you will also want to describe the project.

One tip: Talk about a data science project that is data-driven rather than based on emotion.

“One time, I disagreed with my manager about his proposal for building a dashboard. My manager wanted to jump right in without planning. But I felt spending more time on planning would help us anticipate roadblocks and create an actionable execution plan”.

“I sent my manager an email with my concerns, and during an in-person meeting I was able to show the benefits of planning. Because we had a roadmap and we were solving for future issues, we were able to launch the new dashboard three weeks ahead of our deadline”.

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