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A Beginner's Guide to Answering Behavioral Interview Questions for Data Science Roles

By Krishna Kumar Category Data Science Reading time 7.12 mins Published on Feb 07, 2024

Tips and Tricks to Attempt Behavioral Data Scientist Interview Questions with Ease

Imagine spending 3–4 hours for interview rounds at your desirable MNC for a data scientist position in your domain. Undoubtedly, your skills and hands-on expertise with trending tools helped you easily nail every technical or theoretical question. Yet, after a few days, you are informed about not being selected for that position since you proved unfit!!

Working experts face similar risks now and then. Indeed, data science has become one of the most demanding job roles, changing the whole job landscape. Despite being an expert and skilled with data science trends, many fail to crack the interviews. Unlike earlier times, data scientist interview questions consist of behavioral questions to check the cultural fit of candidates. This is where most candidates fail if they overlook refining behavioral skills.

Regardless of domain, candidates must go through behavioral test rounds and technical and HR interviews. Yet, most experts overlook this round and fail to grab the offer. This blog will break down each facet of behavioral interviews for data science roles in MNCs.

What is a Behavioral Interview? Why is it Vital for Candidates to Clear Interviews?

Many MNCs have started including behavioral interview rounds as an effective metric to assess a candidate. Concerning the thriving data science domain, recruitment and selection criteria see a vital change. Many MNCs conduct behavioral tests to check the cultural fit of the experts.

Behavioral interview is a process of assessing the learning and approaches of candidates in their past projects. When applying for data science jobs, experts showcase past work experience, projects handled, and decisions made. Employers or hiring managers attempt to assess the candidate’s behavior while working on projects. These behavioral questions differ from the basic data science interview questions.

The intent is to assess –

  • The candidate’s personality
  • Mindset while making decisions
  • Ability to fit into a culturally diverse workforce
  • Adaptability to changes

The interview results depict a candidate’s ability to cope with the company’s culture, accept changes, and face critical cases on the job. In short, behavioral interviews attest to an expert’s hands-on expertise with a culturally fit validation.

Tips and Tricks to Answer Behavior Interview Questions for Data Science Roles

Data science interview questions for freshers and experienced pros see changes as per the changing selection metrics. This section will advise a method to practice with behavioral questions to crack a job in any MNC.

Clearing technical/statistics interview questions for data science is hassle-free for many experts. Yet, candidates who face a behavioral interview round for the first time find it hard. The major hurdle they face is not finding a proper way to answer the query. Here, we suggest a step-by-step guide that will assist in cracking the interview.

Step 1: Get Clarity with the Job Description

An image shows an interviewer is interviewing a candidate.

Job description (JD) is vital for any job interview in any firm. It discusses the details of the job role, duties, pay scales, skills, and the firm's expectations from the candidate. A better knowledge of the JD guides the candidate in preparing for obvious behavioral questions.

Example: You applied for a data scientist role in an MNC. The JD states the duty of helping managers with data visuals to reach better decisions. Here, you can expect behavioral questions like – ‘Tell me about your experience where you used data science tools to help decision-makers make a move.

Tip: In such types of questions, try to be a storyteller. Storytelling skills will help you cover end-to-end portions with full details keeping the employer interested.

Pros opting for data science jobs carry relevant work experience in other fields. Plus, their practical experience with live projects is also crucial to clear interviews with MNCs. Reviewing past projects and listing the options can help you better improve your answering skills during behavioral interviews.

Example: In the past, you worked as a marketing analyst in an MNC. You worked on a customer service management project and you applied your data science skills and knowledge to best resolve the matter. Concerning this role, you may get a question, ‘Tell us the ways you opted for resolving customer complaints and queries using relevant data.

Tip: For particular project-related questions, try to provide numerical data (resolved 20% of queries with insightful solutions), and explain your personal opinions, and experiences. This keeps the employer engaged with your answers.

Step 3: Structure Your Responses for Selection

We all have answers to the interview questions, but we fail to perform well under pressure and nervousness. Thus, one must decide and structure the responses to the possible queries. This will save time and effort to perform better with data scientist interview questions in your domain. As a result, selection chances increase in desired job roles in MNCs.

Example: Candidates often face a common question that tests their cognitive skills, critical thinking, and responsiveness over a case. ‘Share your experience with a project or project team, where you encountered a problem. How did you respond to that situation?’ — such questions test your ability to make unbiased moves in tough situations.

Tip: Try to apply the ‘STAR Method’ in such questions. STAR is decoded as Situation, Task, Action, and Results. Candidates can practice these behavioral interview questions with this method to succeed.

  • Situation – Data missing from the data sets arranged.
  • Task – To cut down data deletion rates and create a data set for better predictions.
  • Action – Imputation (replacing deleted data with substitutes) and data arrangement strategies.
  • Results –Unbiased data clean and lesser data deletion

Step 4: Review your Responses to Make them Effective and Concise

An image shows a human hand working on a table and using a laptop.

Answering statistics interview questions for data science or passing the technical rounds is easy. Yet, responding to behavioral questions to present oneself as culturally fit becomes a challenge. Here, candidates are advised to review their responses beforehand and keep them short and crisp. One must try to keep the answers under 2 minutes, adding crucial details.

Example: Suppose you are asked a question – ‘In a project, you suggested an approach that was later denied by one of your team members. How did you respond to a situation of disagreement? Did the change in your approach make changes to the project?’ These questions are common, yet challenging. Thus, one must review the responses to cut them short and effective.

Tip: Maintain a sheet with possible behavioral questions and write your answers. Review and practice in front of the mirror to check the time. If any answer takes more than 2 minutes or seems irrelevant, discard it. This way you can improve the credibility of your answers. Also, while answering try to point out your troubleshooting skills that helped in the past.

Basic data science interview questions are easy to crack for experts. Working pros opting for data science roles fail to crack the job when facing behavioral questions. With the changing times, employers and managers look forward to more culturally fit experts to handle challenging cases. You can enhance your skills and responsiveness with these tips.

In a Nutshell,

The rise of data science in firms and jobs has changed its landscape with thriving scopes that one must have. Working experts find data science as a promising career with better job options, attractive salary packages, and proven career transition. Yet, pros fail to crack the jobs due to a lack of ability to face behavioral interview questions.

Data science interview questions for freshers and experienced pros now go through 3 steps –

  • HR round
  • Technical round
  • Behavioral round

Hence, experts eager to cherish their data scientist careers must try to upgrade their skills to attend behavioral interviews. Regular practice with various interview questions and attending online mock interviews can help. Also, experts can enroll in a Master’s in CS: Data Science and AI program to enrich their knowledge. Its Job Assist Program guides learners with mock interviews that will help them clear behavioral interview rounds.

Plus, upskilling with this master’s degree attests to your career with a globally recognized degree from Woolf University followed by dual certification (IBM & Microsoft).


How should I prepare for a data science interview at MNCs?

A data science interview needs consistent preparation and quality learning. You must start your preparation with some background knowledge of data science and current trends. Check the job openings in this field as per domain and list useful ones along with the trending data science skills. Based on the results, get some online available interview questions (HR, technical, & behavioral) and practice thoroughly.

How do you learn data science questions answers easily?

Basic data science interview questions consist of theoretical and practical concepts. A knowledge of data cleaning, machine learning algorithms, and data visualization is useful to ace interviews. One can learn these questions well by regular practice, improving responses, and appearing in mock interviews online.

What is the behavioral interview question for data scientists?

Behavioral interview questions for data scientists are developed to assess experts' personality traits, behavioral characteristics, and decisiveness skills These questions relate to past project experiences that check the behavioral approaches of an expert.

What kind of questions are repeatedly asked in a data science interview?

Data science interview questions consider evaluating a candidate’s theoretical, practical, and behavioral skill sets. Some important questions are –

  • Define confusion matrix.
  • Briefly explain neural network fundamentals.
  • How do you explain the p-value in the null hypothesis?
  • How do you consider resolving a project-based problem using data science tools?
  • Share your recent project experience.
  • How do you keep pace with the changing data science trends, technologies, and techniques?