Picture this: You’ve just finished a six-hour data science interview and you nailed every technical question, even that one about writing a KNN algorithm from scratch!
But a few days later the recruiter gets back to you: application rejected.
Many times these rejections happen in spite of brilliant technical prowess, often because you didn’t spend enough time preparing for data science behavioral interview questions. Behavioral interview questions are discussion-based and seemingly benign, proving deceptively easy to study for. However, these questions are asked to gather specific information about your skills and experiences like:
You will do better with behavioral questions if you learn what interviewers look for in your responses. A strong answer to a behavioral question is structured, clear and tells a story.
You can use a framework to structure answers in behavioral interviews. For example, the STAR model is one of the most common. Using the STAR framework, you would structure your answers like this:
Data Science interview questions frameworks don’t have to be this rigid every time. Just remember, as a baseline, if you provide an overview of the problem and then go into the details of your approach/how you solved the problem, your answer will be effective.
Here is an example answer for a behavioral question using the STAR framework:
Tell me about a time when you used data science to inform a business decision.
Situation. My previous company, Jobr, was like Tinder for jobs: a swiping-based dating app. A user would sign up, fill in their information, add a resumé, and start getting compatible job recommendations, instead of romantic matches.
Task. Our goal was to increase the number of applications submitted, because the company was paid on a per-job basis.
Action. I built a recommendation system that allowed users to see more relevant jobs. Our baseline system was a Naive Bayes model. It worked, but we had to manually tag it. The one that I designed used an elastic search model. It would take a user’s previous job titles and education, and weight them to different degrees. The model performed a flexible query, which was then weighted, and we would apply that to all of the jobs within a 50-mile radius of the user’s location.
Then, we A/B tested the elastic search model against the Naive Bayes model, to be certain it was better. We gave a small sample of users the elastic search, and ran the test for two weeks.
Results. After those two weeks, we saw a 10-percent lift in job applications using the elastic search model.
See what a difference a little structure can make? Instead of giving the flat answer “I designed a job recommendation system and we saw a ten percent increase in job applications”, we elaborated where necessary to give the interviewer a deeper glimpse into the project.
Instead of getting bogged down in the details of how the recommendation system worked, we provided a few salient data points “It weighted their current position more than their previous one,” “We applied the query to all of the jobs that were in our system within a 50-mile radius” to give the interviewer a practical working knowledge of the system.
Most importantly, my answer took the form of a story with a beginning “I was working at Jobr”, middle “I designed a recommendation system”, and end “We saw a ten percent lift in applications”.
The best answers in behavioral interviews are like stories. They are framed from beginning to end, and include plenty of interesting detail. Your goal should be to leave the listener satisfied on their initial question, and possibly provide material for them to dig further into.
When most people think of “behavioral questions,” they are often thinking of the kinds of questions asked by recruiters, not hiring managers. This includes recruiter questions about being OK with a certain location or commute. It’s not a particularly tough question to “pass” (just don’t say no).
But most behavioral questions that actually matter occur during the hiring manager screen and the onsite interview. With behavioral interview questions, candidates have to:
Technical communication is assessed through questions about your past data science projects, your experience, and how you secure buy-in for projects. In your responses, focus on describing projects in layman’s terms.
The key to answering a question like this is specificity. Explain a technical project you worked on, and describe the exact process you used to ensure all stakeholders understood what you were conveying.
For example, you might say, “In a previous job, I was asked to recommend predictive models the company could use for credit-risk analysis. I created a presentation looking at the three most common types. Using visualizations, I was able to design approachable models of the science and math behind random forest, k-nearest neighbor (KNN), and decision trees. For each visualization, I outlined the benefits and potential risks, and ultimately recommended k-nearest neighbor as the most appropriate predictive model for the analysis.”
This data visualization interview question assesses your ability to back up your work with data, and make those insights accessible to stakeholders. You might talk about working on a dashboard, a particular design scheme you used for visualizations, or a daily reporting process you designed and utilized.
This question boils down to: how do you communicate the business value of your work?
Here is a sample answer to this question. An excerpt is included here, “I was working at a healthcare company. Our goal was to improve user acquisition, and one strategy we tested was adding a “Subscribe to Our Newsletter” button in the footer of blog posts. After rolling the feature out, the number of subscribers wasn’t growing.”
“My job was to understand why the feature wasn’t working. Diving into the analytics, I found that the page scroll depth was just 50-75% for most of our content. Additionally, the average session duration was just 2-3 minutes. I made recommendations to the content marketing team to shorten articles so they were fully read and to move the opt-in higher on the page. After these changes, the opt-in rate increased by 50%.”
You can be honest with a question like this. Tell them if presenting is something you love to do, if you’re so-so, or if it’s a skill you’re working on. However, the real key is describing the process you use to present data science projects.
Say: “I am mostly comfortable with it. But in large group settings it’s more difficult. For me, preparation is critical. First, I gather information about the audience and what their goals are. Then, I develop slides and a loose speaking script for the presentation. Lastly, I like to run through the presentation with colleagues once or twice for clarity and relevance.”
It is incredibly easy to boast about yourself when writing a resumé nowadays. It’s almost a feature of the genre. I frequently find myself amazed at how many resumés seem to be packed with buzzwords and projects that have greatly influenced their companies revenue goals.
I once read a resume that claimed that the applicant had worked on a project that increased monthly revenue by 30% for their business. I was skeptical, and I kept asking questions. It turned out the business was an e-commerce SaaS company and the increase in monthly revenue happened between November and December of the year the person had worked there.
I asked them what happened in the month of January after the holiday season and suddenly they didn’t have as much to say on the impact of their project.
Don’t get caught in the resumé inflation trap! When you put things on your resume, it is important that you can back them up with what you actually worked on and the details that matter to an employer. If you work in an analytics capacity, you need to be able to explain the business decisions and detail behind the projects that influenced certain metrics you achieved. If you work in an engineering capacity, you have to be able to explain exactly how you or your team built a project that delivered business value.
For example, if I were to ask you about a deep learning propensity model that you built, you can bet I would ask you more about how it was trained, the decisions between using different models, and exactly how well it performed when deployed into production. As a candidate, you have to be able to back up your claim and explain exactly how they came about. If you start fluffing on some of the details, then that’s a red flag for an interviewer.
First, ask a clarifying question: What is the definition of a large dataset? This won’t necessarily change how you respond, but it does show your thoroughness. Then, talk about a project using the STAR format we covered at the beginning. Describe the challenges you encountered, like missing or incorrect data, what strategies you used to solve those problems, and lastly provide any lessons you learned from the project.
One Tip: If you have never worked with a large dataset (millions or billions of data points), don’t say no. Instead, use a project that was similar in scope. For example, you might have worked with a smaller dataset, but ran into challenges. Then, define how your experience would apply to a large-scale dataset.
A question like this is basically asking: How do your previous experiences align to this role? For a question like this be very specific about your prior job functions and accomplishments. You might say something like:
“In my previous job, I gathered customer feedback from multiple platforms, including social channels, call center data, and the website. My main function was to aggregate this data, analyze it, and regularly report on it. In my time with the company, my reporting helped the product team continually refine and improve customer experiences. For example, I was able to help the product team refine its user segmentation, and as a result, we were able to create features that were specifically designed for that demographic slice. This resulted in a 10% lift in customer retention.”
A question like this is common in Amazon data science interviews and with other e-commerce companies, and it assesses your ability to add value through your work. Your focus should be on aligning data with an objective business result.
For example, you could say something like: “In my previous job, I worked for an insurance company. I helped to build a predictive model that accurately identified when a customer was experiencing difficulties. Using this model, the customer success team could reach out and resolve these issues as they were arising, not just after the customer was deeply frustrated already. The project helped reduce customer churn by 8%.”
An interviewer asks this question to see if you can set achievable goals, and your approach to planning and achieving them. You might choose a career/learning goal, e.g. mastering the SciPy library, or a job-specific goal like reducing churn by X%.
Be sure to describe why you chose the goal, your plan for achieving it, and any of the challenges that arose. You could say: “I had struggled with data science presentations, and my goal was to improve and get better. There were two ways I achieved this: I enrolled in a self-paced public speaking course, and I took every opportunity to present in my last job. The most recent presentation I gave was a huge success, and helped our team secure buy-in for a new data science initiative.”
Culture fit means a lot of different things for a lot of different companies, but for technical hires it generally means few things:
But this is a really tough question to answer. Mainly because each company is so different. If you are interested in finding a company whose values align with your own, Key Values helps match software engineers to different startups based on the top values each company holds.
This question gets asked to see if you have strong communication skills and know how to use them. A few tips:
An example answer might be: “In a previous data science role, the company had been using a Naive Bayesian model to perform matching. I suggested that an elastic search model might perform better. At first, the team wasn’t open to testing a new approach. However, I created a short presentation on how we could implement and A/B test the elastic search model quickly. After running the A/B test, the elastic model was the clear winner, and we were able to scale up the approach to eventually supplant the original Bayesian approach.”
Behavioral interviews have many questions that deal with ambiguity. Basically, the interviewer wants to know if you can make decisions in unclear situations without strict guidelines, and that you can balance objectives with competing priorities and moving deadlines.
Here is a sample answer: “I joined a start-up as a data scientist, but the role wasn’t clearly defined. The company empowered me to define the objectives of the role by myself. Because the day-to-day responsibilities weren’t yet assigned, I knew it would be best to align my work to the company’s core objectives, which were to grow user engagement by X%, improve marketing performance, and reduce customer churn. I developed a plan with milestones to make incremental quarterly progress towards those goals using data science techniques and communicated the objectives to my manager for feedback and approval. My manager agreed that the work and role was aligned with company objectives and continued to give me new leeway in tackling emerging issues and tasks.”
With this question, the interviewer is looking for emotional maturity, responsibility and validity. One important thing to note: Avoid subjective examples that rely on emotions, something like “I disagreed with how the data science manager was conducting the day-to-day operations because I felt they were distrustful and overbearing”. This shows the interviewer that you rely on emotion and subjectivity to form your disagreements..
A better example is a disagreement rooted in an objective and data-driven approach. You could say: “One time, I disagreed with my manager over the process for building a dashboard, as their approach was to jump straight into the execution. I knew that it would be better to perform some planning in advance, rather than feeling our way through and reacting to roadblocks as they arose, so I documented a plan that could potentially save us time in development. That documentation and planning showed where pitfalls were likely to arise, and by solving for future issues we were able to launch the new dashboard three weeks early.”
Understanding what stakeholders need from you is important in any data science job, and questions like this assess your ability to align your work to stakeholder needs. Describe the processes that you typically utilize in your response, you might include tools like:
Ultimately, your answer needs to convey your ability to understand user and business needs, and how you bring stakeholders in throughout the process.
Practice. See a technical question relating to sentiment analysis on Interview Query.
In data science jobs, you will inevitably face challenges or roadblocks in your work. Behavioral questions provide a chance to assess how you respond to these obstacles. Adaptability questions cover time management, conflict resolution, how you respond to failure, and your openness to learning new skills.
Interviewers want to know how you adhere to deadlines and seek approval for prioritization of your tasks. First, you might talk about how you worked with team members and your manager to prioritize tasks. Second, you’d want to discuss how you balance competing priorities.
Your answer might look something like: “I create a schedule for the week of all the tasks I have to do, based on highest to lowest priority. I also schedule a list for each day, while providing space for ad hoc projects or to work ahead. One time, I had to deliver code for an assignment by the end of the week, but I felt that the code needed an additional five hours to be fully optimized. Each day, I left one hour free to perform code optimization, which helped to improve our final output.”
Choose an example project and use the STAR format to structure your answer:
Interviewers are looking for a few things here: Your resilience, how you go about improving, and how you respond in the moment to failure.
For example, you could say:
“I had to give a presentation to stakeholders about a recent data science project. However, I didn’t make the presentation accessible to the audience. As I was presenting, I could tell that I was losing the audience, so I abandoned the script and skipped ahead to the visualizations, findings and recommendations sections, and I was able to win some of them back. However, the presentation would have been much stronger had I spent more time preparing and aligning my material to my audience. I made sure to incorporate more visuals and outcomes into my future presentations to these stakeholders.”
When answering this behavioral question, focus on the second part and show the interviewer that you’re ambitious, that you don’t give up, and that you face failure by asking, “What went wrong and what can I do better?”
You might say something like: “In my last position at an e-commerce company, I was given a goal of developing a system to predict customers who were most likely to churn, which the company could then use to send personalized promotions to head off those customers. The goal was to increase customer retention by 20% and revenue by 10% over one year. Unfortunately, I missed that goal, with retention only increasing by 10% during the desired timeframe. I wanted to understand what went wrong, so I focused on improving and optimizing the prediction algorithm while also working with the marketing team to better personalize the offers.” If true, let the interviewer know if your response helped you accomplish additional goals set for you in the future.
Demonstrating technical competency generally requires two things: strong technical skills and strong communication skills. You can have all the technical knowledge in the world and it won’t mean anything if you can’t effectively communicate it. Ironically, there is often miscommunication and disagreement on what it means to have “strong communication skills.”
Let’s look at an example:
Once, while we were interviewing a candidate for a data science internship role, we asked them to describe a project they were proud of.
The candidate enthusiastically responded with the following:
Candidate: Yeah, so, for our CS 100 class, we were divided into teams and to build these robots that would battle out in an arena. And basically our robot won the competition and my TA said my group’s robot was really good.
Me: Okay… How did you end up building it?
Candidate: We used Java.
Me: What made your robot good?
Candidate: Well, it won the competition.
You would be surprised how many people say something similar to this when asked about their projects. The problem with the candidate’s response isn’t that the example they chose wasn’t a good example, it’s that they described their accomplishments without actually diving into any of the details that make the experience worth describing. As the interviewer, we have no way to know if the approach the candidate took could be scaled and applied to the projects we need them for at our company, or if in fact the success was a fluke or attributable to a teammate.
As an interviewer, almost ALL we care about are the details.
I don’t care if the intern programmed a robot that won their class competition. I care about the strategies they used to do it. An interview is really about allowing an employer to understand exactly how you think so they can judge whether the way you think and exercise judgment can be applied on the job.
That’s not to say that diving into the details is all you have to do. If you are interviewing for an analytics role and find yourself only talking about the intricacies of SQL queries that you’ve churned out, getting bogged down in those details isn’t going to help you in the interview. Nor does diving into the nitty-gritty make much sense if you’re an engineer talking about building a new deep learning model either.
Rather, the best project descriptions are like stories that are framed from beginning to end that dive into the necessarily interesting parts and leave the listener satisfied and maybe even wanting to learn more. This means balancing the different parts of the project: neither diving straight into something extremely technical nor glossing over the details that make the project applicable to possible future assignments.
Again, the key is defining the problem, how you approached it, challenges that arose, and the results. Using STAR, your answer might look something like this:
“Previously, I was consulting with a company that provided loans for second-hand cars. They were trying to automate and speed up the process of loan approval. Basically, I was dealing with a binary classification with an imbalanced class problem, because there were far more approved applications than rejected applications.
Since the data was imbalanced, I decided to use F1 as the performance metric as it reduces both false positives and negatives…”
See the full script on Towards Data Science.
A question like this is testing your data sense, and ability to adjust your expectations if the data doesn’t support what you initially thought you would see. Data-driven decisions should be supported by facts, not by intuition. Therefore, with a question like this, you would first discuss the project and then go into the specific validation strategies you used.
“In a previous role, I was required to build a recommendation engine to better match products to customers. I felt I had chosen the right algorithm that would lead to better matching and an increase in sales. However, while A/B testing the new recommendation engine, we couldn’t find a statistically significant difference between the new model and the existing one. While disappointing, I took those results and tweaked my assumptions on how to better match products and customers in our next round of recommendation engine testing.”
This type of question assesses technical competency and technical communication skills. You should be thorough in your response and cover:
Finally, discuss the results and cover anything you would change or test if you had more time or resources.
Here are some additional behavioral questions for data science interviews you can use to practice.
See a full guide to answer this type of question on LiveCareer. Here is a sample answer:
“For me, going ‘above and beyond the call of duty’ is really about going all out to get a job done properly. For example, I once worked on a large team project where I could tell that one of my colleagues was struggling with his tasks. To some extent, my work was dependent on his, and it’s probable that had he delivered late, I may have been forced to as well. I approached him discreetly over lunch and offered to stay late and help him out. He couldn’t thank me enough. We worked through the night and in the end, he was able to deliver on time, and so was I.”
Here’s a sample response to a resolving conflict question from Indeed:
“I was working as a project manager on an IT project, and one technician was constantly late finishing tasks. When I approached him about it, he reacted defensively. I kept calm and acknowledged that the deadlines were challenging and asked how I could assist him in improving his performance. He calmed down and told me that he was involved in another project where he had to do tasks that were not in his job description. After a meeting with the other project manager, we came to a resolution that alleviated the technician’s workload. For the remainder of the project, the technician delivered great work.”
In interviews, always have a data science project handy to talk about. For this question, choose a project that you are currently working on or that you recently completed. Here are some tips for getting started on a data science project:
This question might be more of a technical data science question. However, this phrasing makes it more discussion-based. Be sure you add how you learned to use a particular tool; this will help you show your continual-learning mindset. Start with the tools you are strongest with and can answer the most follow-up questions about, and acknowledge your relative comfortability with tools you may have less mastery of.
You might face this as a follow-up to any of the project-related questions above. One tip: For any projects that you cite, have ideas about how you could improve them. Some topics you can cover in your answer include accessing unbiased data, experimenting with stacked models, or testing different classification types.
The person you choose could be a colleague, a supervisor, a teacher or a mentor. The interviewer is looking for insights into your values and what inspires you. You could say:
“The data science manager at my previous company has had a lasting impact on my career. He has such a deep understanding of data science, machine learning and statistical analysis, but is always looking for opportunities to share and impart that wisdom on others. By providing this knowledge and tools, he inspires innovative results and constant growth from his team.”
A question like this shows how passionate you are about data science and your willingness to learn new subjects. You can talk about:
You will make your answer even stronger if you can cite an example of something specific you learned and how you applied it to your work.
This is another question that assesses your continuous learning mindset and passion for data science, and your answer can also show you are curious and collaborative. Cite any open source projects or Github repositories you contribute to, as well as what you’ve learned from contributing. This question also assesses how you spend your time outside of work.
Not active in open source projects? You can cite close examples like self-guided projects or Kaggle competitions. Again, provide a brief overview of your work, but more importantly, cover what you learned.
Expect this question or a variation like, “Why are you the right fit?” Your job is to convince the interviewer that you will deliver, that you are the right culture fit, and that your skills and experience are a perfect match.
You might say: “I have the exact mix of machine learning and Python skills you are looking for. However, beyond my technical skills, I excel at collaborating cross-functionally, which I know will help me jump right in and start delivering on Day 1. Most importantly, this company cares deeply about innovation, and that is what I find most exciting about this role. I am always looking for chances to experiment, grow my skills and find new ways to apply data science to business objectives.”
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